| Literature DB >> 35379234 |
Ruoxin Zhang1, Jianfeng Pei1, Yanli Wang2, Lei Wang1, Yeerzhati Yeerjiang1, Haifeng Gao3, Wanghong Xu4.
Abstract
BACKGROUND: The shortage of healthcare workers is becoming a serious global problem. The underlying reasons may be specific to the healthcare system in each country. Over the past decade, medicine has become an increasingly unpopular profession in China due to the heavy workload, long-term training, and inherent risks. The ongoing COVID-19 pandemic has placed the life-saving roles of healthcare professionals under the spotlight. This public health crisis may have a profound impact on career choices in Chinese population.Entities:
Keywords: COVID-19; Medical study; Motivation; Parents; Senior high school students
Mesh:
Year: 2022 PMID: 35379234 PMCID: PMC8978502 DOI: 10.1186/s12909-022-03309-7
Source DB: PubMed Journal: BMC Med Educ ISSN: 1472-6920 Impact factor: 2.463
Fig. 1Selected constructs of the expectancy-value model for medical career preference in Chinese senior high school students
Characteristics of student participants and their interest in pursuing medical study prior and post the COVID-19 outbreak
| Characteristics | Total (%) | Before COVID-19 | After COVID-19 | Difference | χ | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| No | Percentage | IQR | No | Percentage | IQR | |||||
| All subjects | 21,085 | 3682 | 17.5 | (13.9, 20.9) | 6249 | 29.6 | (23.6, 35.6) | 12.1 | ||
| Sex | ||||||||||
| Male | 9933 (47.1) | 1583 | 15.9 | (11.4, 19.7) | 2658 | 26.8 | (20.6, 31.2) | 10.9 | ||
| Female | 11,152 (52.9) | 2099 | 18.8 | (15.7, 22.3) | 3591 | 32.2 | (27.5, 38.4) | 13.4 | ||
| Father’s education | ||||||||||
| Below primary school | 1727 (8.2) | 276 | 16.0 | (10.7, 21.3) | 538 | 31.2 | (18.3, 43.8) | 15.2 | ||
| Junior school | 5831 (27.7) | 962 | 16.5 | (12.0, 20.4) | 1767 | 30.3 | (24.6, 35.8) | 13.8 | ||
| High school | 5934 (28.1) | 1060 | 17.9 | (13.2, 20.7) | 1774 | 29.9 | (24.2, 35.0) | 12.0 | ||
| Diploma | 2972 (14.1) | 534 | 18.0 | (12.8, 22.3) | 900 | 30.3 | (23.6, 38.0) | 12.3 | ||
| University or above | 4621 (21.9) | 850 | 18.4 | (13.4, 25.6) | 1270 | 27.5 | (23.0, 34.4) | 9.1 | ||
| Mother’s education | ||||||||||
| Below primary school | 2868 (13.6) | 446 | 15.6 | (12.3, 23.6) | 886 | 30.9 | (24.8, 39.2) | 15.3 | ||
| Junior school | 6234 (29.6) | 1061 | 17.0 | (12.7, 19.7) | 1886 | 30.3 | (23.2, 34.6) | 13.3 | ||
| High school | 4933 (23.4) | 853 | 17.3 | (10.6, 22.0) | 1443 | 29.3 | (21.2, 36.3) | 12.0 | ||
| Diploma | 3339 (15.8) | 641 | 19.2 | (11.2, 22.5) | 1007 | 30.2 | (21.7, 35.3) | 11.0 | ||
| University and above | 3711 (17.6) | 681 | 18.4 | (16.6, 28.2) | 1027 | 27.7 | (23.8, 39.5) | 9.3 | ||
| Region | ||||||||||
| Hubei | 809 (3.8) | 100 | 12.4 | 167 | 20.6 | 8.2 | ||||
| Non-Hubei | 20,276 (96.2) | 3582 | 17.7 | (14.4, 21.1) | 6082 | 30.0 | (24.0, 35.7) | 12.3 | ||
| Academic year | ||||||||||
| Year 1 | 7032 (33.4) | 1193 | 17.0 | (13.5, 19.7) | 2155 | 30.6 | (24.2, 34.8) | 13.6 | ||
| Year 2 | 6698 (31.8) | 1019 | 15.2 | (10.6, 18.8) | 1789 | 26.7 | (19.8, 35.3) | 11.5 | ||
| Graduate year | 6984 (33.1) | 1373 | 19.7 | (12.9, 20.6) | 2158 | 30.9 | (22.9, 35.3) | 11.2 | ||
| Resit of graduate year | 371 (1.8) | 97 | 26.1 | (0, 30.8) | 147 | 39.6 | (9.1, 100.0) | 13.5 | ||
| Academic performance | ||||||||||
| Top tier | 14,698 (69.7) | 2695 | 18.3 | (14.3, 21.7) | 4352 | 29.6 | (24.0, 34.8) | 11.3 | ||
| Second tier | 4679 (22.2) | 733 | 15.7 | (10.8, 20.0) | 1402 | 30.0 | (21.9, 36.6) | 14.3 | ||
| Third tier | 687 (3.3) | 117 | 17.0 | (0, 20.0) | 239 | 34.8 | (25.0, 46.6) | 17.8 | ||
| Others | 1021 (4.8) | 137 | 13.4 | (0.0, 21.1) | 256 | 25.1 | (6.3, 37.5) | 11.7 | ||
| Any acquaintance with COVID-19 | ||||||||||
| Yes | 328 (1.6) | 49 | 14.9 | (0, 33.3) | 82 | 25.0 | (0, 50.0) | 10.1 | ||
| No | 20,757 (98.4) | 3633 | 17.5 | (14.0, 20.9) | 6167 | 29.7 | (23.5, 35.8) | 12.2 | ||
| IDSHL score | ||||||||||
| ≤ 73 | 10,759 (51.0) | 1500 | 13.9 | (10.5, 16.5) | 2761 | 25.6 | (19.8, 31.0) | 11.7 | ||
| >73 | 10,326 (49.0) | 2182 | 21.2 | (16.8, 25.5) | 3488 | 33.8 | (27.7, 39.1) | 12.6 | ||
aTotal number of students in all or subgroups and percentage of the subgroup
bNumber of students who selected medicine
cPercentage referring to the percentage of students who selected medicine in the subgroup, each value represents the percentage
dIQR: Interquartile range, represented by the Q1 and Q3 value from the 36 schools with more than 100 participants
eDifference calculated as the subtraction the percentage of students selecting medicine during COVID-19 from the percentage before the outbreak
fχ2 and P values for McNemar tests in each subgroup
Fig. 2Preference toward medical study and epidemic status of COVID-19 across regions in China. A. Distribution of the cumulative number of COVID-19 cases (up to March 4th 2020) and percentage of students (S) or parents (P) selecting medical study; B. correlation of the percentage of students selecting medical studies and the percentage of accumulated positive COVID-19 case in each of 10 provinces accounting for the national accumulated cases; C. correlation of the percentage of parents selecting medical study and the percentage of accumulated positive COVID-19 case in each of 9 provinces accounting for the national accumulated cases; D. correlation of the percentage of students changed to selecting medical study and the number of daily reported new cases across 10 provinces; E. correlation of the percentage of parents changed to selecting medical study and the number of daily reported new cases across 9 provinces
Logistic regression analysis for underlying factors associated with medical study in the students’ dataset
| Characteristics | No. | Univariate analysis | Multivariate analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| β | SE | OR (95%CI) | β | SE | OR (95%CI) | ||||||
| Sex | |||||||||||
| Male | 9933 | Ref | Ref | Ref | Ref | ||||||
| Female | 11,152 | 0.260 | 0.030 | 1.30 (1.22–1.38) | 0.254 | 0.03 | 1.29 (1.22–1.37) | ||||
| Father’s education | |||||||||||
| Below high school | 7558 | Ref | Ref | Ref | Ref | ||||||
| High school | 5934 | −0.030 | 0.040 | 0.97 (0.90–1.05) | − 0.013 | 0.04 | 0.99 (0.91–1.07) | ||||
| Diploma or above | 7593 | −0.090 | 0.040 | 0.91 (0.85–0.98) | −0.100 | 0.05 | 0.90 (0.82–1.00) | ||||
| Mother’s education | |||||||||||
| Below high school | 9102 | Ref | Ref | Ref | Ref | ||||||
| High school | 4933 | −0.060 | 0.040 | 0.94 (0.88–1.02) | −0.034 | 0.04 | 0.97 (0.89–1.05) | ||||
| Diploma or above | 7050 | −0.080 | 0.030 | 0.93 (0.86–0.99) | −0.055 | 0.05 | 0.95 (0.86–1.04) | ||||
| Region | |||||||||||
| Hubei | 809 | Ref | Ref | Ref | Ref | ||||||
| Outside of Hubei | 20,276 | 0.500 | 0.090 | 1.65 (1.39–1.96) | 0.270 | 0.09 | 1.31 (1.10–1.56) | ||||
| Academic year | |||||||||||
| First year | 7032 | Ref | Ref | Ref | Ref | ||||||
| Second year | 6698 | 0.193 | 0.040 | 0.82 (0.77–0.89) | −0.199 | 0.10 | 0.82 (0.67–1.00) | ||||
| Graduate year | 6984 | 0.012 | 0.040 | 1.01 (0.94–1.09) | −0.004 | 0.04 | 1.00 (0.92–1.08) | ||||
| Resit of graduate year | 371 | 0.396 | 0.110 | 1.49 (1.20–1.84) | 0.349 | 0.04 | 1.42 (1.31–1.53) | ||||
| Academic performance | |||||||||||
| Top tier | 4698 | Ref | Ref | Ref | Ref | ||||||
| Second tier | 4679 | 0.017 | 0.037 | 1.02 (0.95–1.09) | 0.025 | 0.039 | 1.03 (0.95–1.11) | ||||
| Third tier and others | 1708 | 0.030 | 0.056 | 0.97 (0.87–1.08) | 0.009 | 0.059 | 1.00 (0.90–1.13) | ||||
| Any acquaintance with COVID-19 | |||||||||||
| No | 20,757 | Ref | Ref | Ref | Ref | ||||||
| Yes | 328 | 0.237 | 0.128 | 0.79 (0.61–1.01) | 0.059 | 0.14 | 1.06 (0.81–1.40) | ||||
| IDSHL scoreb | |||||||||||
| ≤73 | 10,759 | Ref | Ref | Ref | Ref | ||||||
| >73 | 10,326 | 0.395 | 0.030 | 1.48 (1.40–1.57) | 0.418 | 0.03 | 1.52 (1.43–1.61) | ||||
aAdjusted by all the variables as listed in the characteristics
bIDSHL: Infectious disease-specific health literacy
cThe adjusted P value based on Benjamini-Hochberg FDR test
Fig. 3Motivational and de-motivational factors to select medical study in Chinese senior high school students. A. distribution of motivations for medical study; B. Distribution of motivation domains based on expectancy-value model; C. distribution of de-motivations for medical study (%)