Literature DB >> 34851980

Effects of health education on adolescents' non-cognitive skills, life satisfaction and aspirations, and health-related quality of life: A cluster-randomized controlled trial in Vietnam.

Sangchul Yoon1,2, Shinki An3, Dave Haeyun Noh4, Le Thanh Tuan5, Jongwook Lee6.   

Abstract

OBJECTIVE: The effectiveness of health education on adolescents has been questioned, along with a growing body of empirical studies documenting the absence of behavioral changes after the intervention. However, evidence on its impact on other crucial health domains, besides health practices, is lacking. We evaluated the causal effects of a school-based health education program on adolescents' multidimensional psychological health factors.
DESIGN: A cluster-randomized controlled trial.
METHODS: We conducted a cluster-randomized controlled trial involving 140 lower secondary schools in Vietnam. After stratifying by district, schools were randomized 1:1 to either treatment or control groups. Students enrolled in the treatment schools received monthly stand-alone health education in five topics by school teachers at the class level, but control group students did not receive any intervention. The primary outcomes of the study were students' non-cognitive skills, life satisfaction, aspirations gap, and the Health-Related Quality of Life at five-month follow-up. We estimated the intention-to-treat effects with the panel fixed effects model using student panel data.
RESULTS: Of the 6,477 students enrolled at baseline, 2,958 (92%) treated and 2,967 (91%) control students completed the follow-up survey five months after baseline data collection from October to December 2018. Compared with controls, health education led to improved treatment school students' self-efficacy (p-value = 0.013), presumed life satisfaction five years from the present (p-value = 0.001), aspirations gap for a socially and mentally healthy future (p-value = 0.036), and the Health-Related Quality of Life (p-value = 0.036).
CONCLUSION: A school-based health education program enhanced students' non-cognitive skills, life satisfaction and aspirations gap, and the Health-Related Quality of Life significantly. This study proposes essential psychological factors that should be taken into account when evaluating the effectiveness of a health education program in resource-limited settings.

Entities:  

Mesh：

Year:  2021        PMID： 34851980      PMCID： PMC8635366          DOI： 10.1371/journal.pone.0259000

Source DB:  PubMed          Journal:  PLoS One        ISSN： 1932-6203            Impact factor:   3.240


Introduction

Adolescents are a vulnerable group in public health, along with rapid physical and emotional changes resulting from increased hormones and social context changes during puberty. Transitioning from primary to secondary schools, the chance of engaging in risky health behaviors surges substantially as teenagers encounter older students with distinct group norms and peer pressure. However, adolescents often initiate risky behaviors without knowing potential consequences of such actions. Most of the teenagers’ sexual activities are unprotected [1], while sexual debut during adolescence [2] and an increasing proportion of the young population experiencing premarital sex [3] are widely reported. The majority of the cigarette smoking population starts smoking from adolescence [4], when peer pressure plays a significant role. Besides, teenagers tend to show low adherence levels in exercising preventive measures, such as handwashing [5] and physical activities [6] to avoid infectious diseases, myopia, and obesity. While adolescence is a critical stage of the life cycle that requires social protection, suboptimal levels of attention and care are often provided in Low- and Middle-Income Countries (LMICs) due to limited resources, cultural barriers, and the policymakers’ lack of interest. For example, uncorrected refractive errors among adolescents are a primary cause of vision impairment in Vietnam, where about 20 percent of lower secondary school students are estimated to have myopia [7]. Despite increasing abortion rates, Vietnamese adolescents are excluded from the national population policy, leading to approximately 20 percent of abortions involving teens [8]. Competitive environments in school leave little room for adolescents to spend time on outdoor activities in the middle-income country, where more than 85 percent of teenagers do not spend sufficient time on physical activities [6]. The cost of risky health behaviors is substantial for both individuals and societies. Engaging in unprotected sex at a young age may lead to Sexually Transmitted Infections (STIs) [9], mental health problems [10], or unplanned pregnancies [11], causing severe health outcomes to teenagers. In particular, unwanted pregnancies of teenage girls are an urgent global challenge given its subsequent problems, such as complications during pregnancy and delivery [12], unsafe abortions [1], interrupted schooling [13], and loss of future earnings [14]. Likewise, smoking in adolescence may incur severe health issues—respiratory illnesses [15], interruption in brain development [16], and impairment in working memory [17]—which leads to limited job opportunities in the future [18]. The short-run and long-run adverse effects of risky behaviors are reported in preventive health domains, namely not washing hands at critical times [19] and not engaging in outdoor physical activities [6, 7]. Besides, risky health behaviors of teenagers increase the burden on societies by escalating health care costs while losing human capital. A significant amount of taxpayers’ money is spent on social problems attributed to unintended pregnancies [20], more than five percent of global health expenditure goes to smoking-related healthcare costs [21], and malnutrition-related healthcare claims up to USD 3.5 trillion per year globally [22]. Human capital loss caused by risky health behaviors is considerable. Apart from approximately 20 percent and 11 percent of global deaths resulting from communicable, maternal, neonatal, and nutritional (CMNN) diseases [23] and smoking [24], respectively, risky health behaviors deprive societies of human capital accumulation as adolescents become pregnant [25], smoke [16, 17], experience a vision problem [26], have an unbalanced diet [27], and suffer from an infectious disease [28]. Health education is a widely observed intervention designed to prevent such damaging health behaviors of teenagers in both LMICs and High-Income Countries (HICs). One of the underlying assumptions of health education is that an economic agent engages in unsafe health practices because her perceived immediate benefits are greater than the perceived future costs of such behaviors [29]. Hence, health education aims to prevent risky health behaviors by updating the agent’s perceived future costs resulting from the risk factors. The majority of the information-based programs target teenagers, when individuals’ health attitudes and behaviors are formed [30, 31]. In particular, schools are a vital place to implement a health education program to reach a large number of adolescents for years [32, 33] in a financially sustainable and logistically convenient way thanks to existing learning structures [34], ensuring the high returns to the intervention [35]. However, the effectiveness of health education has been questioned, along with a mounting body of empirical evidence documenting the null effects of the information-based approach in behavioral changes. Despite a few studies presenting significant effects [36, 37], the overwhelming consensus from existing empirical evidence is that a health education program may increase teenagers’ knowledge in health, but translating it into behavioral changes is exceptionally challenging. The findings are consistent across topics, including sexual and reproductive health [38], anti-smoking [32], eye health [39], hygiene [40], and nutrition [41], leading to the conclusion that cost-effectiveness of health education is an ‘illusion’ [42]. Although much of the literature focused on the effects of health education on teenagers’ Knowledge, Attitudes, and Practices (KAP) in health, there exists limited evidence on psychological factors related to adolescents’ current and future health outcomes, such as non-cognitive skills, the quality of life, and life satisfaction and aspirations gap. For example, self-efficacy, personal beliefs in own capacity [43], is an essential mediating component necessary when translating knowledge into action [44] as it shapes one’s behavioral intentions. A teenager’s life satisfaction elicits how healthy a student is physically, mentally, and socially, which are associated with school life [45], other risky health behaviors [46], social problems, and mental health in both positive [47] and negative [48] ways. Finally, the Health-Related Quality of Life (HRQoL), an individual’s self-perceived multidimensional health domains [49] beyond morbidity and mortality, captures both the self-assessed physical and mental health status of adolescents, serving as an indicator of current health and a predictor of future health outcomes [50]. In this study, we examined the effects of a health education program on non-cognitive skills, life satisfaction, aspirations gap, and HRQoL besides health KAP. We conducted a randomized-controlled trial in Vietnam to investigate the impacts of school-based health education on adolescents’ psychological health which should not be neglected when evaluating the information-based approach. Randomly selected lower secondary school students in Thanh Hoa province received monthly stand-alone health education in five topics: Eye Health; Sexual and Reproductive Health (SRH); Infectious Diseases and Handwashing; Food and Nutrition; and Anti-Smoking at the class level. Treated students learned essential health information and life skills necessary to make sound health decisions from trained school teachers. We assessed impacts of the health education program by comparing the treatment students to their control group counterparts five month after baseline data collection.

Methods

Study designs, randomization, and participants

We conducted a cluster-randomized controlled trial in lower secondary schools in Thanh Hoa province, Vietnam, from 2018 to 2019. From all of 652 public lower-secondary schools across the province, 140 schools were randomly selected based on the total number of lower-secondary schools in each district (Fig 1). The schools were assigned to either the treatment (70 schools) or the control (70 schools) groups after stratifying by the district. Randomization took place at the school level rather than the student level to take into account spillover effects and to minimize potential ethical issues. All the selected treatment and control schools agreed to participate in the program. We distributed two types of consent forms for students to take home—one about the health education program participation to all treatment school students and another about survey participation to a subset of treatment and control school students. Of these, students who returned the form signed by their parent or guardian were enrolled in the program and the study. All the participating students in the treatment group received a series of health education sessions, while none of these was provided to the control group students.
Fig 1

Study area.

This figure plots study schools and district boundaries in Thanh Hoa province, Vietnam. The treatment schools are denoted by solid circles, while the control schools are denoted by hollow circles. Source: Government of Viet Nam.

Study area.

This figure plots study schools and district boundaries in Thanh Hoa province, Vietnam. The treatment schools are denoted by solid circles, while the control schools are denoted by hollow circles. Source: Government of Viet Nam. In each school, approximately 48 students were randomly sampled for surveys after stratifying by school class and sex of students. Inclusion criteria for the study were the randomly selected student cohorts in grades 6, 7, 8, and 9 in the 2018–2019 academic year (aged 11 to 14) whose caregivers gave consent for their children to participate in the surveys. Also, students were required to provide assent and be able to read and speak Vietnamese fluently to join the study. Our analytic sample includes those who completed both baseline and follow-up surveys. However, including students who were surveyed at follow-up regardless of baseline survey participation in the sample does not change results significantly. Exclusion criteria were students who refused assent; whose parents or guardians declined for their children to join the study; and those who were unable to speak or read Vietnamese. Students who had been transferred to other schools between baseline and follow-up surveys were also excluded from our analysis. Ethical approval for the study was obtained from the Yonsei University Institutional Review Board (IRB ID: 4–2018-1060) and the University of Minnesota Institutional Review Board (IRB ID: STUDY00004327).

Procedures

Once a month, trained teachers instructed a 45-minute-long health education session in the treatment schools at the class level as a stand-alone course over five months. A total of five health topics, namely Eye Health, SRH, Infectious Diseases and Handwashing, Food and Nutrition, and Anti-Smoking, took place sequentially on regular school days. Each session consisted of two parts—lectures and in-class activities. A teacher started each session by explaining what constitutes risky health behaviors, why they matter, and how to prevent them (i.e., lecture), followed by student-centered participative activities (i.e., in-class activities) when students learned essential life skills to protect their own health from damaging health behaviors. The health program aimed to reduce risky health behaviors of participating adolescents, including unprotected sex, sugar overconsumption, and cigarette smoking, and to increase their adherence to preventive health practices, namely outdoor activities and washing hands at critical times. The program was designed to enhance students’ health knowledge and attitudes by updating their perceived future costs associated with such behaviors. Given the improved understanding and perspectives in risky behaviors, life skills acquired from in-class activities were to enable them to avoid risky behaviors. Along the way of obtaining crucial information and life skills, health education was expected to promote students’ non-cognitive skills as their beliefs in personal ability to control their own behaviors improve, leading to increased life satisfaction and HRQoL. Before providing health education to students, we trained treatment school teachers to serve as health education instructors via the Training of Trainers. Two health teachers from each treatment school recruited by headmasters were invited to two-day training sessions. Using the teaching guidelines approved by the Department of Education and Training, professors at the Thanh Hoa Medical College led the training sessions. During the training sessions, the teachers learned what to teach (i.e., health promotion messages) and how to teach (i.e., pedagogical skills) using the guidelines. After completing training, the health teachers had organized another workshop at the school level, serving as peer educators for homeroom teachers who delivered health promotion messages to students at the class level. The homeroom teachers taught all health topics except SRH, which health teachers instructed, given the sensitivity of the topic. Pre- and post-training evaluation reveals that the trained teachers had a better understanding of teaching materials after completing training. We collected a rich array of data, such as students’ demographic information (e.g., age, sex, ethnicity, mother tongue, and the number of household members living together), school life, health KAP in five topics, non-cognitive skills, life satisfaction, aspirations gap, and HRQoL from in-person surveys. Moreover, students’ health information such as height, weight, chest circumference, vision acuity, hearing ability, blood pressure levels, and dental problems, was collected from the treatment school students immediately after the baseline survey. The follow-up survey took place from March to April 2019, approximately five months after baseline data collection from October to December 2018, but the second follow-up survey scheduled to be collected in 2020 was interrupted due to the COVID-19 pandemic.

Outcomes

We measured outcomes at the individual level. The primary outcomes of the study were non-cognitive skills (i.e., self-esteem and self-efficacy), life satisfaction and aspirations gap, and HRQoL, and the secondary outcomes were health knowledge and practices in the five topics students had learned. We calibrated levels of life satisfaction and aspirations gap by using an adapted version of the Cantril Ladder [51], where respondents were asked to indicate where they thought that they were at the present time and five years from the present using a zero (the worst) to nine (the best) scale. Students’ aspirations gap was computed as the difference between the expected future life satisfaction and the current life satisfaction. The KINDL-R questionnaire [52] was used to measure HRQoL such as students’ physical well-being, emotional well-being, self-esteem, family, friends, and school life. Students’ health knowledge scores were constructed by using the two-parameter logistic Item Response Theory model [53], and we used students’ self-reported answers for health practice outcomes in our analysis. For sensitive health practice questions such as sexual intercourse and smoking, students had an option to choose “I do not know,” which was coded as missing.

Statistical analysis

The unit of analysis was an individual student. We estimated the intention-to-treat (ITT) effects where the impacts of offering the health program were evaluated regardless of compliance. First, we assessed whether the baseline characteristics of the treatment and control groups were statistically different. We then examined the treatment effects of health education by the panel fixed effects model, since students who had been surveyed at baseline were visited again for the follow-up survey. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline to report standardized effect sizes. Theoretically, the randomization allows us to estimate unbiased treatment effects without covariates. However, we included some key individual characteristics—students’ age, the number of siblings, and the number of rooms per household member—as control variables in addition to the student fixed effects, mainly due to baseline imbalances between the treatment and control groups to increase precision. Time-invariant characteristics, such as ethnicity and locality of schools, were excluded from the vector of covariates because the student fixed effects control for any differences attributed to factors that do not change across time. Throughout the analysis, standard errors were clustered at the school level—the unit of randomization. We used Stata version 15.1 for statistical analysis with 5 percent statistical significance level criteria.

Results

Details of the study sample are demonstrated in Fig 2. Of the 6,477 enrolled students at baseline, 5,925 students (2,958 treated and 2,967 control students) who had completed an assessment at five months were included in the study. The attrition rates of the treatment and control groups were eight percent and nine percent, respectively, but the differences in the baseline characteristics of the lost students to follow-up were marginal, and they were not statistically significant at the 5 percent level.
Fig 2

Trial profile.

The follow-up survey took place approximately five months after the baseline survey. Students who had been surveyed both at baseline and follow-up were included in the sample.

Trial profile.

The follow-up survey took place approximately five months after the baseline survey. Students who had been surveyed both at baseline and follow-up were included in the sample. Table 1 presents baseline characteristics of the study participants by treatment status. Panel A confirms that the two groups were balanced, on average, except for a few variables with small differences in magnitude given a large sample size. About half of the respondents were female, and the average age of participants was between 12 and 13 years, reflecting the random sampling stratified by gender and class. The average household size was 4.7 for both groups, and 51 percent of respondents were the first child of the families. More than 97 percent of students in our study sample had answered that they had lived with at least one of their parents, and we did not find any statistical difference between the treatment and control groups. Despite the random assignment of schools, we found four demographic variables—ethnicity, language, the number of siblings, and the number of rooms per person—that were statistically different across groups. While the magnitude of the differences was small, we controlled for the number of siblings and the number of rooms per person when evaluating the treatment effects to increase precision, but ethnicity and language were excluded because of the student fixed effects that partial out any effects of time-invariant variables. Panel B reports two school characteristics—school size and locality—across treatment conditions. The average school size was about 270 students, which was in line with the General Statistics Office of Vietnam statistics [54] as a result of the random sampling of schools. The treated schools were more likely to be located in rural areas, but the difference was small, and the fixed effects model addresses any time-invariant factors, including locality.
Table 1

Student and school characteristics.

TreatmentControlt-test
(N = 2,958)(N = 2,967)(N = 5,925)
MeanSDMeanSDp-value
Panel A: Demographic Characteristics
 Female (0–1)0.510.500.520.500.351
 Age (Years)12.791.1912.761.200.321
 Ethnicity: Kinh (0–1)0.750.430.790.41<0.001
 Language: Vietnamese (0–1)0.770.420.820.38<0.001
 First Child (0–1)0.520.500.510.500.683
 Number of Household Members4.751.464.741.350.585
 Number of Siblings1.641.241.491.03<0.001
 Number of Rooms/person0.530.310.550.300.003
 Living with Both Parents (0–1)0.890.310.880.320.436
 Living with Other Guardians (0–1)0.030.170.030.180.313
 Living with Mother Only (0–1)0.060.230.060.240.641
 Living with Father Only (0–1)0.020.160.020.150.810
Panel B: School Characteristics
 School Size (Number of Students)270.99120.40282.53116.690.520
 Rural (0–1)0.860.350.770.420.171

Note: The sample includes students who participated in both the baseline and the follow-up surveys. The p-values from the t-test of the null hypothesis that H0 : β1 = 0 in the regression Variable = β0 + β1 × Treat + DistrictDummies + ϵ are reported as randomization took place at the district level.

Note: The sample includes students who participated in both the baseline and the follow-up surveys. The p-values from the t-test of the null hypothesis that H0 : β1 = 0 in the regression Variable = β0 + β1 × Treat + DistrictDummies + ϵ are reported as randomization took place at the district level. We also conducted balance tests for both primary and secondary outcome variables. While Table 2 shows that some variables are statistically different across groups given a large number of observations, magnitudes are small, and the student fixed effects take into account any time-invariant pre-treatment differences.
Table 2

Balance test for dependent variables.

TreatmentControlt-test
(N = 2,958)(N = 2,967)(N = 5,925)
MeanSDMeanSDp-value
Panel A: Non-cognitive Skills
 Self-Esteem (0–100)70.7718.8269.3918.660.004
 Self-Efficacy (0–100)69.1713.9269.9313.720.019
Panel B: Life Satisfaction
 Present (1–9)6.541.646.461.610.062
 Future (1–9)7.431.447.521.390.010
 Aspirations gap (Future-Present)0.891.561.061.55<0.001
Panel C: Health-Related Quality of Life
 Aggregated (0–100)69.2310.6768.9710.860.420
 Physical Well-being (0–100)72.4814.9773.7015.520.001
 Emotional Well-being (0–100)74.1715.1673.6815.610.293
 Self-esteem (0–100)54.7720.7553.5520.120.020
 Family (0–100)80.6715.1581.0514.910.279
 Friends (0–100)74.3116.2473.8116.540.320
 School (0–100)58.9616.8158.0016.960.025
Panel D: Health Knowledge
 Aggregated (0–100)62.489.2562.748.360.224
 Eye (0–100)57.9115.9657.2114.990.100
 SRH (0–100)55.0715.2654.3714.150.067
 Handwashing (0–100)77.9715.8179.9415.42<0.001
 Food & Nutrition (0–100)42.8813.7442.9313.460.910
 Anti-Smoking (0–100)78.5516.0479.2315.070.083
Panel E: Health Practices
 Outdoor Activities (Likert, 1–5)3.411.033.401.020.675
 Had Sex (0–1)0.040.210.020.15<0.001
 Handwashing, Eating (0–1)0.960.200.970.170.002
 Handwashing with Soap, Eating (0–1)0.880.330.860.340.225
 Handwashing, Toilet (0–1)0.960.210.970.170.004
 Handwashing with Soap, Toilet (0–1)0.910.290.910.290.570
 Snacks (Likert, 0–5)3.891.413.801.430.018
 Had Smoked (0–1)0.040.200.030.170.044

Note: The sample includes students who participated in both the baseline and the follow-up surveys. The p-values from the t-test of the null hypothesis that H0 : β1 = 0 in the regression Variable = β0 + β1 × Treat + DistrictDummies + ϵ are reported as randomization took place at the district level.

Note: The sample includes students who participated in both the baseline and the follow-up surveys. The p-values from the t-test of the null hypothesis that H0 : β1 = 0 in the regression Variable = β0 + β1 × Treat + DistrictDummies + ϵ are reported as randomization took place at the district level. Panel A of Fig 3 summarizes the treatment effects on students’ non-cognitive skills, life satisfaction and aspirations gap, and HRQoL. Despite the insignificant effects on self-esteem, we found that the health education program increased students’ perceived beliefs in their own capacity by 0.081 SDs (p-value = 0.013). Besides, students’ life satisfaction increased substantially after receiving health education on five topics. The current life satisfaction of students was 0.038 SDs (p-value = 0.281) higher in the treatment group than the control group, but it was not statistically significant at the 5 percent level. However, when the students were asked where they thought they would stand five years from the present, the treated students’ expectation regarding their future was increased by 0.129 SDs (p-value = 0.001) compared to the control group, leading to the 0.075 SDs (p-value = 0.036) higher aspirations gap after receiving health education. We then assessed the effects of the school-based health education program on students’ HRQoL. Overall, we found positive treatment effects on all aspects of HRQoL. While physical well-being was the only HRQoL sub-component that had a significant treatment effect, we found positive coefficients for all the other sub-components, leading to 0.067 SDs (p-value = 0.036) higher aggregated HRQoL scores from the treatment school students than the control group.
Fig 3

Treatment effects.

Coefficients and confident intervals estimated from the panel fixed effects model are plotted. Standard errors were clustered at the school level. Students’ age, the number of siblings, and the number of rooms per household member were included as control variables in addition to the student fixed effects. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline. Students’ knowledge levels were constructed by using the two-parameter logistic IRT model. Students who had been surveyed both at baseline and follow-up were included in the sample.

Treatment effects.

Coefficients and confident intervals estimated from the panel fixed effects model are plotted. Standard errors were clustered at the school level. Students’ age, the number of siblings, and the number of rooms per household member were included as control variables in addition to the student fixed effects. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline. Students’ knowledge levels were constructed by using the two-parameter logistic IRT model. Students who had been surveyed both at baseline and follow-up were included in the sample. We also investigated how the school-based health program had affected primary outcomes of the existing health education literature: students’ health knowledge and practices. Findings from this study are consistent with a rapidly growing body of empirical evidence: significant effects of health education on adolescents’ knowledge but limited effects on behavioral changes. Overall, students’ health knowledge increased significantly after receiving monthly health education (Panel B of Fig 3). Student’s aggregated health-related knowledge increased by 0.054 SDs (p-value<0.001) mostly attributed to the effects on Eye Health (β=0.059, p-value = 0.020), SRH (β=0.102, p-value<0.001), and Infectious Diseases and Handwashing (β=0.065, p-value = 0.004). While we found higher knowledge scores in Food and Nutrition (β=0.019, p-value = 0.313) and Anti-smoking (β=0.024, p-value = 0.291) from the treated students relative to the control group counterparts, the differences are not statistically significant. Table 3 shows mixed results for behavioral changes despite the positive treatment effects on students’ knowledge in health. While students reduced risky behaviors in two areas, Infectious Diseases and Handwashing and Food and Nutrition, they did not change health behaviors in Eye Health, SRH, and Anti-smoking. First, students’ handwashing behaviors improved significantly after receiving health education (Columns 3–6). The percentages of teenagers who answered that they wash their hands before eating a meal (β=0.019, p-value = 0.008) and after using the toilet (β=0.018, p-value = 0.010) with or without soap increased significantly, while the effects on handwashing behaviors with soap were not significant. The null effects on handwashing with soap are consistent with existing studies concluding that improvement in handwashing with soap behaviors requires the provision of soap. Column 7 shows that health education led to significantly decreased sugar consumptions from snacks and soft drinks among adolescents (β=-0.073, p-value = 0.014). However, estimates for the other health behaviors were noisy. After receiving health education, students were more likely to engage in regular outdoor activities at least one hour per day as a preventive measure of myopia (Column 1), but the group difference between the treatment and control students was not significant at the 5 percent level (p-value = 0.224). Finally, we found insignificant program effects on students’ initiation of sexual activity (p-value = 0.517) and smoking (p-value = 0.153) as reported in Columns 2 and 8, respectively.
Table 3

Health-related practices.

EyeSRHHandwashingFoodAnti-Smoking
(1)(2)(3)(4)(5)(6)(7)(8)
OutdoorSexEatingEating (Soap)ToiletToilet (Soap)SnacksSmoked
Treat0.042(0.034)0.002(0.003)0.019**(0.007)-0.001(0.012)0.018*(0.007)-0.004(0.011)-0.073*(0.030)-0.007(0.005)
Mean3.3960.0220.9710.8650.9700.9063.7990.031
SD1.0230.1450.1670.3420.1710.2921.4310.174
FEXXXXXXXX
ControlsXXXXXXXX
R20.00270.01100.00160.00030.00160.00040.00420.0309
N11,84810,69411,84811,84811,84811,84811,84811,506

Note:

*P < 0.05,

**P < 0.01,

***P < 0.001.

Standard errors in parentheses. The panel fixed effects model was used to estimate the treatment effects. Standard errors were clustered at the school level. Students’ age, the number of siblings, and the number of rooms per household member were included as control variables in addition to the student fixed effects. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline. Students who had been surveyed both at baseline and follow-up were included in the sample.

Note: *P < 0.05, **P < 0.01, ***P < 0.001. Standard errors in parentheses. The panel fixed effects model was used to estimate the treatment effects. Standard errors were clustered at the school level. Students’ age, the number of siblings, and the number of rooms per household member were included as control variables in addition to the student fixed effects. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline. Students who had been surveyed both at baseline and follow-up were included in the sample. Table 4 reports heterogeneous treatment effects across gender and age. Panel A shows that, on average, male students benefited from the program more than female counterparts. First, male students’ aspirations gap increased significantly by 0.182 SDs, while the effects on female students are not significant, leading to a statistically significant difference across gender by 0.105 SDs. The differential effects of the program on students across gender are well-manifested in HRQoL outcomes from which the group differences are observed for both aggregated index and sub-components, namely physical well-being, emotional well-being, self-esteem, and school life. The table shows that receiving health education has no significant effects on females students’ most of the HRQoL outcomes, but it has positive effects on male counterparts for all of HRQoL variables except family, leading to heterogeneous treatment effects across gender within the treatment group. Panel B shows that the effects of the health education program were larger for younger students. The treatment effects on the aggregated HRQoL decreased by 0.057 SDs when a student was one year older, and we found similar results from sub-components.
Table 4

Heterogeneity.

Treat × GroupTreatTreat × Group+TreatN
Coef.SEsCoef.SEsCoef.SEs
Panel A: Female
 Non-cognitive Skills
  Self-Esteem-0.0390.045-0.0390.038-0.078*0.03911,850
  Self-Efficacy-0.0280.0480.095*0.0440.0670.03511,850
 Life Satisfaction
  Present-0.0000.0460.0380.0400.0380.04411,850
  Future-0.105*0.0450.182***0.0430.0770.04311,850
  Aspirations gap-0.0930.0550.123**0.0450.0300.04611,850
 HRQoL
  Aggregated-0.188***0.0340.162***0.033-0.0260.03811,850
  Physical Well-being-0.211***0.0460.201***0.045-0.0100.04311,850
  Emotional Well-being-0.131**0.0410.082*0.036-0.0490.04011,850
  Self-esteem-0.190***0.0380.099*0.040-0.091*0.03611,850
  Family-0.0220.0390.0730.0410.0510.03811,850
  Friends-0.0370.0430.077*0.0360.0400.03811,850
  School-0.124**0.0400.104**0.039-0.0200.04011,850
Panel B: Age
 Non-cognitive Skills
  Self-Esteem0.0300.019-0.4560.25411,850
  Self-Efficacy-0.0210.0200.3540.25611,850
 Life Satisfaction
  Present0.0180.019-0.1930.25111,850
  Future0.0210.019-0.1480.25911,850
  Aspirations gap0.0020.0210.0500.28111,850
 HRQoL
  Aggregated-0.057***0.0140.815***0.18911,850
  Physical Well-being-0.042*0.0200.647*0.26611,850
  Emotional Well-being-0.067***0.0160.903***0.21811,850
  Self-esteem0.0240.016-0.3070.21411,850
  Family-0.0240.0170.3820.23311,850
  Friends-0.050**0.0150.723***0.21011,850
  School-0.078***0.0191.074***0.25511,850

Note:

*P < 0.05,

**P < 0.01,

***P < 0.001.

Standard errors in parentheses. The panel fixed effects model was used to estimate the treatment effects. Standard errors were clustered at the school level. Students’ age, the number of siblings, and the number of rooms per household member were included as control variables in addition to the student fixed effects. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline. Students who had been surveyed both at baseline and follow-up were included in the sample.

Note: *P < 0.05, **P < 0.01, ***P < 0.001. Standard errors in parentheses. The panel fixed effects model was used to estimate the treatment effects. Standard errors were clustered at the school level. Students’ age, the number of siblings, and the number of rooms per household member were included as control variables in addition to the student fixed effects. Continuous outcome variables were normalized by the means and standard deviations of the control group values of corresponding variables measured at baseline. Students who had been surveyed both at baseline and follow-up were included in the sample.

Discussion

In this study, we focused on psychological health dimensions in addition to direct health knowledge and practices of adolescents as a result of health education. We reported that a school-based health education program had led to increases in adolescents’ self-efficacy, life satisfaction, aspirations gap, and HRQoL. However, the program had limited effects on reducing risky health behaviors—the primary objective of the information-based intervention. First, our findings showed significant improvement in self-efficacy after receiving a series of health education classes. Combining with limited behavioral changes despite improved health knowledge, this is an important finding from the perspective of behavioral changes. There exist three potential mechanisms through which enhanced health knowledge and self-efficacy were not translated into reduced risky health behaviors. According to the theory of planned behaviors [55], the absence of behavioral changes is caused by students’ lack of intentions to adhere to lessons from health education. In other words, students who received health education had a better understanding of risky health behaviors (i.e., increased knowledge in health), believed that they had the capability to refrain from them (i.e., increased self-efficacy), but they might not have enough intentions or motivations to exercise their efforts, leading to the limited behaviors changes. On the other hand, the limited effects on health practices may reflect circumstantial factors that prevent teenagers from improving preventive measures while avoiding risky behaviors regardless of their intentions. The prototype willingness model [56] from the health psychology literature suggests that adolescents’ intentions play a limited role in health practices because their risky behaviors are more likely to be reactive to risk conducive environments rather than planned actions. For example, even if a student has strong intentions to spend more time on outdoor activities to prevent myopia and obesity, it may require parents and teachers’ permission whose one of the top priorities is academic success in school. Also, a student may be unable to wash their hands with soap despite the enhanced knowledge and attitudes simply because there exists no soap available at home and school as documented in the existing literature. [57]. Although including parents and teachers does not necessarily lead to successful behavior changes of teenagers [58], ensuring conditions under which a teen has an option to make a health decision is a prerequisite condition for a health program to have an impact on adolescents’ health behaviors. Finally, evaluating the treatment effects five months after baseline data collection may not give sufficient time for students to alter health behaviors, in particular in sexual intercourse and smoking, given low baseline prevalence. Despite serious problems caused by the risky behaviors during adolescence, less than four percent of students answered at baseline that they had had sex or had smoked before. Hence, examining the long-run effects is necessary before concluding that health education has no impact on behavior changes since risks of initiating such health behaviors increase substantially as a teen advances from lower secondary to higher secondary schools. Secondly, this study showed the positive impacts of health education on critical health domains that received relatively limited attention in the health education literature: life satisfaction and aspirations gap. A growing strand of literature pays attention to teenagers’ subjective well-being given its strong association with behavioral [59, 60], social life [45, 61], and psychological [47, 61] problems of the core risk group. In particular, life satisfaction is a useful indicator of severe mental health issues, such as depression [62], loneliness [48], and suicide [63]. The significant effects on life satisfaction we found highlight the possibility of the information-based intervention to address mental health problems of adolescents by increasing their hope and aspirations gap for a socially and mentally healthy future. Finally, we found that students’ perceived health, particularly in physical well-being, had improved significantly after participating in the school-based health education program. Despite the importance of HRQoL in assessing adolescents’ current and future health [50], the result should be interpreted with caution. First, the increased physical well-being may reflect mere changes in adolescents’ subjective perceptions rather than improvement in physical health per se because HRQoL was designed to measure a respondent’s self-assessed health status. Second, the present study did not allow us to disentangle the effects on physical well-being attributed to health education from the effects caused by physical examination, since it took place in the treatment schools only. While students’ increased physical well-being may indicate improved physical health as a result of reduced health behaviors, we cannot exclude other channels through which health assessment affected students. For example, the physical examination might improve students’ health status by identifying health issues that they were at risk for, leading to enhanced health conditions at follow-up. At the same time, the physical examination could function as an awakening tool for teenagers to confirm how healthy they were given that most students learned that they did not have health problems in vision (85 percent), hearing (97 percent), and dental (70 percent). Thus, measuring students’ physical health status in the treatment schools may spur participating students into an active assessment of their own health, concluding that ‘I am healthy,’ which could be reflected by higher physical health well-being scores relative to the control group. There exist several limitations in this study. First, as mentioned above, isolating the treatment effects of health education on students from the school-based health check-up was difficult given the study design. While the physical examination was conducted as a part of data collection, it may play a role in behavioral changes if it induces participants to change their attitudes on certain behaviors as shown in existing studies [64, 65]. Second, we examined short-run impacts of the school-based health education program only because additional data collection activities had been interrupted by the COVID-19 pandemic. A further study investigating the long-run effects is necessary to examine whether the health education program failed to achieve behavior changes, and to assess to which extent the treatment effects on psychological factors remain. Finally, our outcomes may be subject to potential social desirability bias, given that we relied on students’ self-reported answers [66].

Conclusion

This study demonstrated significant treatment effects of a health education program on adolescents’ vital psychological health domains: self-efficacy, life satisfaction, aspirations gap, and HRQoL. Taken together with mixed results for health practices, findings on self-efficacy revealed the teenagers’ limited intentions or potential risk-conducive circumstances that may prevent adolescents from avoiding risky health behaviors, shedding light on the last mile to be addressed to incur behavioral changes among the risk group. This study also documented the positive effects of a school-based health education program on psychological health dimensions of adolescents that received relatively limited attention in the health education literature. Significant improvements in students’ life satisfaction, aspirations gap, and HRQoL highlighted the necessity of taking into account the broader health dimensions that should not be neglected when evaluating impacts and effectiveness of a health education program on teenagers in resource-limited settings in LMICs.

The study protocol.

The study protocol is described. (PDF) Click here for additional data file. 4 Aug 2021 PONE-D-21-19671 Effects of health education on adolescents' non-cognitive skills, life satisfaction and aspirations, and health-related quality of life: A cluster-randomized controlled trial in Vietnam PLOS ONE Dear Dr. Lee, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the minor points raised by Reviewer 2 during the review process. Please submit your revised manuscript by Sep 18 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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Reviewer #2: To the authors of this paper, I first want to commend you for writing a very clear and straightforward paper, and making a new contribution to the field of evaluating adolescent education programs for health behavior change. The paper is well written and adds a nice contribution to the literature by asking: if these programs don’t achieve their primary goals (of short-term behavior change), what do they accomplish? I find that a good question to explore. Overall, I think the paper could be greatly strengthened by contextualizing the research question, location of research, constraints faced by the population, and by making clearer ties to the program theory of change. I will elaborate on these points below: The introduction is well researched and synthesizes lots of ways that negative health behaviors can have later health consequences as well as the potential costs to society. My comment on this is for the authors to consider the tone a bit in some of the writing; it currently reads as blaming adolescents for these types of societal outcomes where adolescents rarely have full control of their decisions. So much of their lives is shaped by policy, access (financial, mobility, time, permission), families, schools, wealth/resources, location of living, etc. It would strengthen this paper to reflect on how members of these age groups don’t always have control, so these programs seek to shift behaviors where it seems that teenagers do have some control. The nuance would help move the focus away from adolescences’ lack of interest in being more healthful, but to unpacking why they may carry certain attitudes and behaviors. This unpacking could also lead to thinking about the programmatic theory of change – what can these programs accomplish if focusing only on education of the adolescents? Where are there barriers and constraints? In the introduction, the paper would be strengthened by providing information about the specific population being studied in this trial. What are the national or sub-national statistics for these age groups on the knowledge/attitudes/behaviors that the programs seek to modify? In providing this information, it would help explain why this intervention is important (or an intervention that successfully leads to behavioral changes is important). For example, what are the rates of smoking for these age groups? What % of adolescent population starts sex at the age of this study population? How serious are these problems in the population tested? It would be additionally useful to see this information by age as adolescence is such a dynamic period of time - 11 year olds are very different from 14 year olds on many of the behaviors potentially targeted in the program. In addition, as part of the introduction, beyond painting a picture of the scope of problems for this population in this country/region, it would be helpful to understand if there’s national or subnational commitment or interest in this set of problems around adolescent unsafe or unhealthy behaviors? Are there government commitments or goals in their plans? (or global ones?) Do particular schools see this as a large issue? In describing the program, it would be helpful for the reader if the authors can specify the programmatic goals. What are the specific health behaviors that the program is aiming to reduce/change? What are the actual program objectives and theory of change set out at the beginning of implementation? I recognize that this paper is about looking at the secondary program objectives (eg not health behavior change) but the context of what the program seeks to achieve is important in evaluating its success and whether it should be modified, stopped, scaled up, etc. Within that program description, this would be a good moment in the paper to bring in the proposed theory of change between the primary indicators of interest for this study and the program objectives. For example, do the authors think that the indicators being studied (eg self efficacy) exist on the pathway to health behavior change ultimately? Do the programs seek to improve the measured indicators of this study or are these more unintended positive consequences? And then, making a connection between the problem statement at the intro around adolescent unhealthy behaviors and the outcomes being studied here. Why does it matter that these outcomes may improve? row 183 – As described, the aspirations measure seems to be more like a measure of predicted future, which feels a bit different than aspirations. For example, an adolescent may have a dream to be a doctor but feel too constrained in their environment/lack of resources/mental health (eg depression)/etc to actually believe they can achieve becoming a doctor, so they won’t share that there 5 yr vision includes being a doctor. I wonder if it’s possible to explain a bit more whether this is aspirations (their hopes) or their realistic prediction of the future. I am not an expert to speak to the statistical analysis method employed, so this is a question, not a suggestion. I am just wondering if analysis at the individual level should have some cluster-level coefficients (like ICCs) in the analytical model given each school constitutes a cluster of respondents? Results: The paper would be helped by seeing table on baseline prevalence / rates of behaviors for key measures (primary & secondary outcomes). It’s challenging to interpret results without knowing where they started. For example, are there no statistically significant changes in SRH or smoking behaviors because there's little activity or little smoking going on overall? Perhaps there just isn’t much negative behavior to be reduced, so there wouldn’t be a seen reduction? The paper would also be strengthened in a table that provides the measures used. For example, # of questions that measure what specifically? What behavior in what time frame? How were N/A accounted for? (for example, questions about safe sex behaviors for people not having sex). Results: Is it possible in this data to disaggregate by gender and/or age group? That could be a really helpful analysis of whether girls and boys have very different or similar stats at baseline, and if changes differ by gender, as well as by age group. That could help answer if this type of programming is effective for any sub-groups within the study population. (To this point, the intro statistics on these outcomes of interest at the nationa/sub-national level would also be more interesting if broken down by age and gender). row 323/330 – The authors mention circumstantial factors - that is a really important point and I would suggest delving into that more. What could be prohibiting use of behaviors? This would be very worthwhile exploration in the discussion section – what are the barriers to the behavior change that are not just individual motivation? Access? Policy? Time? Resource? As an example, is there limited option for exercise because students are so busy with schoolwork and help around the house and supporting with taking care of their siblings? I think that exploration could then connect to reflections on what kind of intervention COULD lead to the program objective of behavior change on key behaviors. Last, on this topic of limitations of the intervention design, this reviewer finds that the authors could write a bit more that directly acknowledges that the intervention as designed doesn’t seem to accomplish its primary goal (of changing behaviors). What is in the literature that does led to reductions in smoking commencement, early sexual debut, unprotected sex, etc? Is it about teaching methods? Or frequency/duration of lessons? Perhaps who the teachers are really matters? Could those be incorporated into this type of intervention to make a difference? Otherwise, I think the authors should be frank in the final discussion that perhaps the intervention cannot achieve its stated program objectives – so either a different intervention design is needed, or else people who want to implement these programs need to understand that the programs can achieve interesting outcomes related to self esteem, etc, as shown in this analysis, but the expectation should not be on behavior change. Finally, in the discussion, it is worth talking about how the typical assumption of knowledge --> changed attitudes --> changed behaviors is really challenged by the evaluations of these interventions. Perhaps the authors could talk about how the theory of change needs to be re-evaluated because it clearly isn’t so linear to get results. There’s a robust literature out there on how this assumed linear model does not show itself to be true across many programs with information provision as the primary activity, it would be great to delve into that more. row 375 – The authors mention that the time period is shorter than planned due to the COVID-19 pandemic. It would be helpful to lay that out earlier in methods - I was definitely curious why the 5 month follow up period, and having that it was planned for longer but had to be modified is helpful context for reading further. And then I have a few very small comments from the results section: row 221 - specify the significance level instead of saying “at any conventional levels”. - 5% 10%? row 264 - is that 0.067 SDs difference or scores difference? Text says scores but sentence follows format of others which list SDs, so just want to clarify. row 280 - say stat insignificant instead of “estimated with large standard errors” if that’s what you mean. Thank you for writing a paper on this interesting topic and taking a unique angle to studying these interventions by asking what non-primary outcomes are achieved in these programs. With these modifications, I think this will be a strong contribution to the conversation. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. 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If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Author response: We have reviewed and revised the reference list accordingly. An additional list of references is added to the existing list to incorporate Reviewer 2’s suggestions. Also, we have retracted the following two references by new ones because former ones are no longer available on the websites. Water and Sanitation (UNICEF, 2004) and By the numbers: the public costs of teen childbearing (Hoffman SD, 2006) were replaced by Water, sanitation and hygiene interventions to combat childhood diarrhoea in developing countries (Waddington H et. al., 2009) and Public costs from unintended pregnancies and the role of public insurance programs in paying for pregnancy-related care (Sonfield A and Kost K, 2015), respectively. Reviewers’ Comments to the Authors Reviewer #1: As the statistician reviewing this paper, I have read the paper and I recommend it to be accepted. I have no questions in regards to the data analysis as the analyses were performed rigorously and properly written up in detail for the manuscript. Author response: Thank you! Editor’s Comments to the Authors Reviewer #2: To the authors of this paper, I first want to commend you for writing a very clear and straightforward paper, and making a new contribution to the field of evaluating adolescent education programs for health behavior change. The paper is well written and adds a nice contribution to the literature by asking: if these programs don’t achieve their primary goals (of short-term behavior change), what do they accomplish? I find that a good question to explore. Author response: Thank you! Overall, I think the paper could be greatly strengthened by contextualizing the research question, location of research, constraints faced by the population, and by making clearer ties to the program theory of change. I will elaborate on these points below: The introduction is well researched and synthesizes lots of ways that negative health behaviors can have later health consequences as well as the potential costs to society. My comment on this is for the authors to consider the tone a bit in some of the writing; it currently reads as blaming adolescents for these types of societal outcomes where adolescents rarely have full control of their decisions. So much of their lives is shaped by policy, access (financial, mobility, time, permission), families, schools, wealth/resources, location of living, etc. It would strengthen this paper to reflect on how members of these age groups don’t always have control, so these programs seek to shift behaviors where it seems that teenagers do have some control. The nuance would help move the focus away from adolescences’ lack of interest in being more healthful, but to unpacking why they may carry certain attitudes and behaviors. This unpacking could also lead to thinking about the programmatic theory of change – what can these programs accomplish if focusing only on education of the adolescents? Where are there barriers and constraints? Author response: Thank you for pointing this out. As mentioned by the reviewer, it is very important not to blame adolescents assuming that it is solely adolescents’ responsibility, given that there exist circumstantial factors, barriers, and constraints under which they make a health decision. To incorporate the reviewer’s suggestion, we have added a paragraph elaborating on such exogenous factors that teenagers face from Row 17-29. In the introduction, the paper would be strengthened by providing information about the specific population being studied in this trial. What are the national or sub-national statistics for these age groups on the knowledge/attitudes/behaviors that the programs seek to modify? In providing this information, it would help explain why this intervention is important (or an intervention that successfully leads to behavioral changes is important). For example, what are the rates of smoking for these age groups? What % of adolescent population starts sex at the age of this study population? How serious are these problems in the population tested? Author response: Thank you for the suggestion. We have added statistics on risky behaviors of Vietnamese adolescents (e.g., insufficient outdoor activities) as well as consequences of such behaviors (e.g., myopia, unsafe abortions) in Row 20-29 to explain why the health education program is necessary. Please note that we reported statistics consequences associated with risky behaviors if statistics on the behaviors per se do not reflect what is happening to teens or the necessity of intervention. For example, the reported sexual intercourse prevalence among Vietnamese adolescents under 15 is very low, partly because of under-reporting attributed to social desirability bias and data collection issues. Rather than proving the prevalence of early onset of sexual intercourse for adolescents aged 11-14, we present its potential consequence, unsafe abortions, to show readers what happened in the absence of an intervention. We also presented the baseline prevalence of risky health behaviors in Table 2. It would be additionally useful to see this information by age as adolescence is such a dynamic period of time - 11 year olds are very different from 14 year olds on many of the behaviors potentially targeted in the program. In addition, as part of the introduction, beyond painting a picture of the scope of problems for this population in this country/region, it would be helpful to understand if there’s national or subnational commitment or interest in this set of problems around adolescent unsafe or unhealthy behaviors? Are there government commitments or goals in their plans? (or global ones?) Do particular schools see this as a large issue? Author response: Thanks for pointing this out. Although we appreciated the reviewer’s feedback, we are worried that including too much information about risky behaviors may distract readers from the primary focus of this study—psychological factors. As suggested by the reviewer, it is very important to let readers know why a health program is necessary for Vietnamese adolescents by providing statistics on risky behaviors because the primary goal of a such intervention is behavior changes. Hence, we report the treatment effects of the program on health practices in the manuscript. However, we tried to limit the scope of the study to psychological factors by minimizing information on risky health behaviors per se. Given multiple topics of health education students received, we think that illustrating statistics in detail by age group and government policies on five health areas may distract readers. In describing the program, it would be helpful for the reader if the authors can specify the programmatic goals. What are the specific health behaviors that the program is aiming to reduce/change? What are the actual program objectives and theory of change set out at the beginning of implementation? I recognize that this paper is about looking at the secondary program objectives (eg not health behavior change) but the context of what the program seeks to achieve is important in evaluating its success and whether it should be modified, stopped, scaled up, etc. Author response: Thank you for pointing this out. As suggested by the reviewer, specific health behaviors that the program was designed to tackle are added in Row 161-165, followed by theory of change set out at the beginning in Row 165-173. Within that program description, this would be a good moment in the paper to bring in the proposed theory of change between the primary indicators of interest for this study and the program objectives. For example, do the authors think that the indicators being studied (eg self efficacy) exist on the pathway to health behavior change ultimately? Do the programs seek to improve the measured indicators of this study or are these more unintended positive consequences? And then, making a connection between the problem statement at the intro around adolescent unhealthy behaviors and the outcomes being studied here. Why does it matter that these outcomes may improve? Author response: Thank you for pointing this out. Accordingly, we described the proposed theory of change and how psychological factors that we evaluate are affected by the treatment in Row 165-173. row 183 – As described, the aspirations measure seems to be more like a measure of predicted future, which feels a bit different than aspirations. For example, an adolescent may have a dream to be a doctor but feel too constrained in their environment/lack of resources/mental health (eg depression)/etc to actually believe they can achieve becoming a doctor, so they won’t share that there 5 yr vision includes being a doctor. I wonder if it’s possible to explain a bit more whether this is aspirations (their hopes) or their realistic prediction of the future. Author response: We renamed the term as aspirations gap since it is not just a measure of predicted future but a measure of the gap between future and current levels of subjective wellbeing. We define the aspirations gap as a difference between one’s current and future life evaluation, says subjective general satisfaction. We assumed that this measures one’s level of aspirations for better future life in general, not specific to future jobs or achievements, compared to the current level. The larger gap means that the respondent has greater hope for relatively better life while the smaller gap indicates that the one expects similar level of wellbeing in the future compared to the current level. I am not an expert to speak to the statistical analysis method employed, so this is a question, not a suggestion. I am just wondering if analysis at the individual level should have some cluster-level coefficients (like ICCs) in the analytical model given each school constitutes a cluster of respondents? Author response: We clustered the standard errors at the school level—the level of treatment—to take into account potential issues attributed to the clustering. As a result, our findings are robust to intraclass correlation. Results: The paper would be helped by seeing table on baseline prevalence / rates of behaviors for key measures (primary & secondary outcomes). It’s challenging to interpret results without knowing where they started. For example, are there no statistically significant changes in SRH or smoking behaviors because there's little activity or little smoking going on overall? Perhaps there just isn’t much negative behavior to be reduced, so there wouldn’t be a seen reduction? Author response: Thank you for pointing this out. We have added a table reporting balance test results for dependent variables (Table 2 on page 16) in the manuscript so that readers can see the baseline values of key measures. Also, the mean and standard deviation of each dependent variable from the control group are included in Table 2 for psychological outcomes. The paper would also be strengthened in a table that provides the measures used. For example, # of questions that measure what specifically? What behavior in what time frame? How were N/A accounted for? (for example, questions about safe sex behaviors for people not having sex). Author response: Thank you for pointing this out. We have added a range of values in Tables 1 and 2. Please note that when analyzing the treatment effects, we report normalized effects by standardizing continuous variables by the means and standard deviations of the control group values of corresponding variables measured at baseline. Binary variables were not normalized. For sensitive questions such as sexual intercourse and smoking, students had an option ‘I do not know,’ which was coded as missing. We have added this in the manuscript in Row 217-219. Results: Is it possible in this data to disaggregate by gender and/or age group? That could be a really helpful analysis of whether girls and boys have very different or similar stats at baseline, and if changes differ by gender, as well as by age group. That could help answer if this type of programming is effective for any sub-groups within the study population. (To this point, the intro statistics on these outcomes of interest at the nationa/sub-national level would also be more interesting if broken down by age and gender). Author response: We think that this is an excellent suggestion. We have added our heterogeneous treatment effects analysis in Row 338-355 along with Table 4. Please note that we used a continuous age variable rather than age group dummies to report the marginal treatment effects associated with one unit increase in age. row 323/330 – The authors mention circumstantial factors - that is a really important point and I would suggest delving into that more. What could be prohibiting use of behaviors? This would be very worthwhile exploration in the discussion section – what are the barriers to the behavior change that are not just individual motivation? Access? Policy? Time? Resource? As an example, is there limited option for exercise because students are so busy with schoolwork and help around the house and supporting with taking care of their siblings? I think that exploration could then connect to reflections on what kind of intervention COULD lead to the program objective of behavior change on key behaviors. Author response: We agree that this is a very important point. In the revised manuscript, we delved into potential mechanisms through which a health education program did not lead to behavior changes of adolescents, including circumstantial factors in Row 367-403. Last, on this topic of limitations of the intervention design, this reviewer finds that the authors could write a bit more that directly acknowledges that the intervention as designed doesn’t seem to accomplish its primary goal (of changing behaviors). What is in the literature that does led to reductions in smoking commencement, early sexual debut, unprotected sex, etc? Is it about teaching methods? Or frequency/duration of lessons? Perhaps who the teachers are really matters? Could those be incorporated into this type of intervention to make a difference? Otherwise, I think the authors should be frank in the final discussion that perhaps the intervention cannot achieve its stated program objectives – so either a different intervention design is needed, or else people who want to implement these programs need to understand that the programs can achieve interesting outcomes related to self esteem, etc, as shown in this analysis, but the expectation should not be on behavior change. Author response: We agree that it is important to acknowledge the fact that the intervention did not achieve its primary goal of behavior changes. We have added the following in Row 361-363: “However, the program had limited effects on reducing risky health behaviors—the primary objective of the information-based intervention.” Besides, we have proposed three potential mechanisms through which we did not find behavior changes despite the increased self-efficacy in Row 367-403. Finally, in the discussion, it is worth talking about how the typical assumption of knowledge --> changed attitudes --> changed behaviors is really challenged by the evaluations of these interventions. Perhaps the authors could talk about how the theory of change needs to be re-evaluated because it clearly isn’t so linear to get results. There’s a robust literature out there on how this assumed linear model does not show itself to be true across many programs with information provision as the primary activity, it would be great to delve into that more. Author response: Thank you for pointing out the necessity of revisiting the theory should be re-evaluated. Although we agree that this is an important task to do, our findings do not allow us to through which mechanisms health education failed to cause behavior changes among adolescents. We have proposed three potential mechanisms through which the program was unable to achieve its goal of behavior changes: absence of students’ intentions; the lack of room for students to alter their health behaviors; and the timeframe of data collection. Hence, further study is necessary for us to argue the theory of change should be re-revaluated in a certain way. Besides, we tried to focus on the causal effects of health education on psychological factors that are often neglected when evaluating the information-based approach rather than mechanisms through which health education fails in behavior changes. row 375 – The authors mention that the time period is shorter than planned due to the COVID-19 pandemic. It would be helpful to lay that out earlier in methods - I was definitely curious why the 5 month follow up period, and having that it was planned for longer but had to be modified is helpful context for reading further. Author response: As suggested, the delay in data collection is noted under Procedure in Row 198-200. And then I have a few very small comments from the results section: row 221 - specify the significance level instead of saying “at any conventional levels”. - 5% 10%? Author response: Thank you for pointing this out. We have revised it in Row 250 as follows: “they were not statistically significant at the 5 percent level.” row 264 - is that 0.067 SDs difference or scores difference? Text says scores but sentence follows format of others which list SDs, so just want to clarify. Author response: Thank you for pointing this out. It is SDs as you said, and we have corrected it accordingly in Row 298. row 280 - say stat insignificant instead of “estimated with large standard errors” if that’s what you mean. Author response: Thank you for pointing this out. We have revised it in Row 313-314 as follows: “the differences are not statistically significant”. Thank you for writing a paper on this interesting topic and taking a unique angle to studying these interventions by asking what non-primary outcomes are achieved in these programs. With these modifications, I think this will be a strong contribution to the conversation. Author response: Thank you! Submitted filename: Response to Reviewers.pdf Click here for additional data file. 11 Oct 2021 Effects of health education on adolescents' non-cognitive skills, life satisfaction and aspirations, and health-related quality of life: A cluster-randomized controlled trial in Vietnam PONE-D-21-19671R1 Dear Dr. Lee, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Lindsay Stark Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: 10 Nov 2021 PONE-D-21-19671R1 Effects of health education on adolescents’ non-cognitive skills, life satisfaction and aspirations, and health-related quality of life: A cluster-randomized controlled trial in Vietnam Dear Dr. Lee: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Lindsay Stark Academic Editor PLOS ONE
  34 in total

1.  A randomized intervention of smoking for adolescents in urban Wuhan, China.

Authors:  Chih-Ping Chou; Yan Li; Jennifer B Unger; Jiang Xia; Ping Sun; Qian Guo; Sohaila Shakib; Jie Gong; Bin Xie; Chunghong Liu; Stanley Azen; Jianguo Shan; Huiyan Ma; Paula Palmer; Peggy Gallaher; C Anderson Johnson
Journal:  Prev Med       Date:  2006-02-20       Impact factor: 4.018

2.  Young age at first intercourse and risk-taking behaviours--a study of nearly 65 000 women in four Nordic countries.

Authors:  Tina Bech Olesen; Kirsten Egebjerg Jensen; Mari Nygård; Laufey Tryggvadottir; Pär Sparén; Bo Terning Hansen; Kai-Li Liaw; Susanne K Kjaer
Journal:  Eur J Public Health       Date:  2011-05-19       Impact factor: 3.367

3.  Sexual behaviour in Britain: early heterosexual experience.

Authors:  K Wellings; K Nanchahal; W Macdowall; S McManus; B Erens; C H Mercer; A M Johnson; A J Copas; C Korovessis; K A Fenton; J Field
Journal:  Lancet       Date:  2001-12-01       Impact factor: 79.321

4.  The importance of parental knowledge: evidence from weight report cards in Mexico.

Authors:  Silvia Prina; Heather Royer
Journal:  J Health Econ       Date:  2014-07-22       Impact factor: 3.883

5.  Relationship between life satisfaction and violent behaviors among adolescents.

Authors:  R F Valois; K J Zullig; E S Huebner; J W Drane
Journal:  Am J Health Behav       Date:  2001 Jul-Aug

6.  Beyond age at first sex: patterns of emerging sexual behavior in adolescence and young adulthood.

Authors:  Abigail A Haydon; Amy H Herring; Mitchell J Prinstein; Carolyn Tucker Halpern
Journal:  J Adolesc Health       Date:  2011-11-17       Impact factor: 5.012

7.  Child marriage: a silent health and human rights issue.

Authors:  Nawal M Nour
Journal:  Rev Obstet Gynecol       Date:  2009

8.  From willingness to intention: experience moderates the shift from reactive to reasoned behavior.

Authors:  Elizabeth A Pomery; Frederick X Gibbons; Monica Reis-Bergan; Meg Gerrard
Journal:  Pers Soc Psychol Bull       Date:  2009-05-08

9.  The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years.

Authors:  David Cella; Susan Yount; Nan Rothrock; Richard Gershon; Karon Cook; Bryce Reeve; Deborah Ader; James F Fries; Bonnie Bruce; Mattias Rose
Journal:  Med Care       Date:  2007-05       Impact factor: 2.983

10.  Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016.

Authors: 
Journal:  Lancet       Date:  2017-09-16       Impact factor: 79.321
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