Job demands‐control, job support, and depressive symptoms: Unraveling job support's moderating mechanism among social workers in China
Bibliographic record
Abstract
Abstract Work‐induced stress poses significant mental health risks in high‐stress professions, such as social workers. This study, grounded in the Job Demands‐Control‐Support model and Conservation of Resources theory, explores how job stressors affect social workers' depressive symptoms, focusing on job demands, job control, and their combinations, alongside the moderating role of job support. Analyzing data from the 2019 China Social Work Longitudinal Study through a city‐level fixed‐effects model, we find that job demands (role ambiguity and role conflict) and job control influence depressive symptoms both independently and interactively. Specifically, high job demands increase depressive symptoms, while greater job control reduces them. When looking at the combination of job demands and control, social workers facing low job demands with high job control report the lowest levels of depressive symptoms, followed by those with low demands and low job control. Moreover, coworker support emerges as crucial in reducing depression, especially for those grappling with high‐role ambiguity and job control. Additionally, in high role conflict with low job control scenarios, support from leaders and supervisors is essential for lessening depressive symptoms. These findings highlight the essential role of job support in mitigating the impact of job stressors on social workers' mental health in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".