Meta-Analysis: The Effects of Workload and Social Support on Burnout in Health Workers
Bibliographic record
Abstract
Background: Burnout is a psychological syndrome of exhaustion, cynicism and ineffectiveness at work. Some factors causing burnout are workload and family support. The existence of a high workload can increase the incidence of burnout and high social support can prevent burnout in health workers. This study aims to estimate the effect of workload and social support on burnout in health workers. Subjects and Method: A systematic review and meta-analysis was carried out using the PRISMA guidelines and the PICO model covering Population = health workers; Intervention = high workload and high social support; Comparison = low workload and low social support; Outcome = burnout. Articles were collected from databases such as PubMed, Science Direct, and Google Schoolar. The keywords used in the database search were workload AND "social support" AND burnout OR fatigue AND "health workers" AND "cross sectional".A total of 12 articles met the inclusion criteria for the meta-analysis and were assessed using RevMan 5.3. Results: Meta-analysis from France, Afghanistan, Canada, Uganda, Ghana, Ethiopia, Malawi, Brazil, Malaysia and China showed that high workloads can increase burnout in health workers (aOR= 2.37; 95% CI= 1.10 to 5.10; p = 0.003), high social support can reduce the risk of burnout in health workers (aOR= 0.54; 95% CI= 0.42 to 0.71; p= 0.001), and these results were statistically significant. Conclusion: High workload can increase burnout among health workers and high social support can reduce the risk of burnout in health workers. Keywords: workload, social support, burnout, health workers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".