| Literature DB >> 35391955 |
He Cao1, Kechun Zhang1, Danhua Ye2, Yong Cai3, Bolin Cao4, Yaqi Chen1, Tian Hu1, Dahui Chen5, Linghua Li6, Shaomin Wu7, Huachun Zou7,8, Zixin Wang2, Xue Yang2.
Abstract
Factory workers make up a large proportion of China's internal migrants and may be highly susceptible to job and adaptation stress, negative affective states (e.g., depression and anxiety), and Internet gaming disorder (IGD). This cross-sectional study investigated the relationships between job stress, psychological adaptation, negative affective states and IGD among 1,805 factory workers recruited by stratified multi-stage sampling between October and December 2019. Structural equation modeling (SEM) was conducted to test the proposed mediation model. Among the participants, 67.3% were male and 71.7% were aged 35 years old or below. The prevalence of probable depression, probable anxiety, and IGD was 39.3, 28.7, and 7.5%. Being male, younger age, and shorter duration of living in Shenzhen were associated with higher IGD scores. Job stress was significantly associated with IGD (β = 0.11, p = 0.01) but not with negative affective states (β = 0.01, p = 0.77). Psychological adaptation was significantly associated with negative affective states (β = -0.37, p < 0.001) but not with IGD (β = 0.09, p > 0.05). Negative affective states were positively associated with IGD (β = 0.27, p < 0.001). The indirect effect of psychological adaptation (β = -0.10, p = 0.004) but not job stress (β = 0.003, p = 0.77) on IGD through negative affective states was statistically significant. The observed psychological correlates and mechanisms are modifiable, and can inform the design of evidence-based prevention programs for depression, anxiety, and IGD in this population.Entities:
Keywords: China; Internet gaming disorder; factory workers; job stress; negative affective states; psychological adaptation
Year: 2022 PMID: 35391955 PMCID: PMC8982757 DOI: 10.3389/fpsyg.2022.837996
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
Background characteristics of the participants (N = 1805).
| Background Characteristics |
| % |
|
| ||
| Male | 1214 | 67.3 |
| Female | 591 | 32.7 |
|
| ||
| <26 | 378 | 21.0 |
| 26–30 | 437 | 24.2 |
| 31–35 | 478 | 26.5 |
| >35 | 489 | 27.1 |
| Missing | 13 | 0.8 |
|
| ||
| Large city | 36 | 2.1 |
| Middle/small city | 282 | 14.7 |
| Village | 1253 | 69.6 |
| Missing | 234 | 13.6 |
| Education | ||
| Middle school or below | 1059 | 58.6 |
| High school | 585 | 32.4 |
| College or above | 113 | 6.3 |
| Missing | 48 | 2.7 |
|
| ||
| <3000 | 166 | 9.2 |
| 3000–4999 | 1099 | 60.9 |
| >4999 | 484 | 26.8 |
| Missing | 56 | 3.1 |
|
| ||
| <2 | 495 | 27.4 |
| 2–5 | 527 | 29.2 |
| 6–10 | 318 | 17.6 |
| >10 | 431 | 23.9 |
| Missing | 34 | 1.9 |
Internet gaming disorder (IGD) scores by background status.
| Background variables | IGD scores | ||
| Mean ± SD | F(df)/t(df) | p | |
| Sex | 10.15 |
| |
| Male | 1.06 ± 2.08 | (1919.5) | |
| Female | 0.29 ± 1.28 | ||
| Age | 12.71 |
| |
| <26 | 1.18 ± 2.15 | (3,2003) | |
| 26–30 | 0.81 ± 1.84 | ||
| 31–35 | 0.85 ± 1.97 | ||
| >35 | 0.44 ± 1.54 | ||
| Hometown | 0.43 | 0.65 | |
| Large city | 0.93 ± 1.92 | (2,1744) | |
| Middle/small city | 0.71 ± 1.78 | ||
| Village | 0.80 ± 1.90 | ||
| Education | 0.47 | 0.63 | |
| Middle school or below | 0.77 ± 1.86 | (2,1962) | |
| High school | 0.86 ± 1.93 | ||
| College or above | 0.82 ± 1.92 | ||
| Monthly income | 0.53 | 0.59 | |
| <3000 | 0.94 ± 2.05 | (2,1951) | |
| 3000–4999 | 0.79 ± 1.89 | ||
| >4999 | 0.78 ± 1.84 | ||
| Years in Shenzhen | 3.7 |
| |
| <2 | 0.81 ± 1.83 | (3,1980) | |
| 2–5 | 0.98 ± 2.05 | ||
| 6–10 | 0.79 ± 1.92 | ||
| >10 | 0.60 ± 1.74 | ||
SD = standard deviation.
Bold value means a p-value less than 0.05.
Bi-variate correlations between psychological variables and IGD.
| 1 | 2 | 3 | 4 | |
| 1 Job stress | 1 | |||
| 2 Psychological adaptation | −0.35 | 1 | ||
| 3 Depressive symptoms | 0.15 | −0.32 | 1 | |
| 4 Anxiety symptoms | 0.21 | −0.28 | 0.65 | 1 |
| 5 IGD | 0.11 | −0.09 | 0.21 | 0.21 |
**Correlation is significant at the 0.01 level (2-tailed).
SD = standard deviation
FIGURE 1The proposed meditation model with standardized path coefficients.