Relationships between actual and desired workplace characteristics and job satisfaction for community health workers in China: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Community health workers are the main providers of community health services in China and have been important in the process of health system reform that has been in place since 2009. Therefore, it is critical that healthcare managers and policy decision makers motivate current staff and improve their job satisfaction. This study examined workplace characteristics and their contribution to job satisfaction in community health workers in Heilongjiang Province, China. METHODS: A cross-sectional survey of 448 community health workers, from three cities in Heilongjiang province, was conducted between October 1, 2012 and December 31, 2012. Multistage sampling procedures were used to measure socioeconomic and demographic status, job satisfaction, and both actual and desired workplace characteristics. Factor analysis was conducted to determine the main factors contributing to workplace characteristics, and multiple linear regression analysis was performed to assess the key determinants of job satisfaction. RESULTS: Eight groups of factors were identified as the most important workplace characteristics. These comprised system and policy; fringe benefits; work itself; work relationships; professional development; recognition; work environment; and remuneration. In all cases, all desired workplace characteristics were higher than the associated actual workplace characteristics. The main determinants of job satisfaction were occupation, years worked in health service institution, and five subscales representing the gap between desired and actual workplace characteristics, which were system and policy; fringe benefits; working relationship; professional development; and remuneration. CONCLUSIONS: These findings suggested that managers wishing to enhance job satisfaction should assess workplace characteristics comprehensively and design mechanisms that reduce the gap between actual and desired workplace characteristics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".