Meta-Analysis: Effects of Workload and Work Environment on Work Satisfaction in Health Personnel
Bibliographic record
Abstract
Background: Job satisfaction is one of the important points to motivate and improve work efficiency, high job satisfaction can improve the performance of health workers and patient satisfaction. However, low job satisfaction results in fatigue and a tendency to increase the turnover of health workers which will exacerbate the condition of health facilities. The research objective was to analyze the effect of workload and work environment on job satisfaction in health workers. Subjects and Method: This study is a meta-analysis with PICO. Population: health workers. Intervention: high workload and safe work environment. Comparison: low workload and unsafe work environment. Outcome: job satisfaction. The articles used in this study were obtained from three databases namely Google Scholar, Science Direct and Pubmed. The keywords used to search for articles are “Workload” OR “Job Overload” AND “Safe Work Environment” AND “Job Satisfaction” AND “Health Workers” AND “Multivariate”. The articles used were full text in English from 2012 to 2022. Articles were selected using the PRISMA flowchart and analyzed using the RevMan 5.3 application. Results: A total of 17 cross-sectional study articles from Ethiopia, Switzerland, Israel, Belgium, China, Canada and Denmark. Based on the analysis, health workers with high workloads reduced job satisfaction 0.47 times compared to health workers with low workloads and this was statistically significant (aOR=0.47; 95% CI=0.24 to 0.92; p=0.030). Health workers with a safe work environment increased job satisfaction 2.75 times compared to health workers with an unsafe work environment and this was statistically significant (aOR=2.75; 95% CI=1.59 to 4.78; p=0.003). Conclusion: High workload reduces job satisfaction in health personnel and a safe work environment increases job satisfaction in health personnel. Keywords: workload, work environment, job satisfaction
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".