Slack resources and individual performance of clinicians: the mediating role of job satisfaction and empirical evidence from public hospitals in Beijing, China
Bibliographic record
Abstract
BACKGROUND: Clinicians in Chinese public hospitals face a complex and severe clinical practice environment, and the individual performance of clinicians is key to improving the output of the healthcare industry. This study aims to explore the mechanism of slack resources in improving individual performance of clinicians and the role of job satisfaction in this process, while the study framework is based on the widely applied Job-Demands Resources theory. METHODS: Based on the study framework composed of slack resources, individual performance, and job satisfaction, hypotheses have been put forward, and questionnaires have been distributed to representative clinicians in tertiary public hospitals. Finally, 318 valid data collected from clinicians have been obtained. To verify the hypotheses, multiple linear regression models have been established to explore the relationship between variables, and the three-stage regression models have been used to verify the presence of mediating role. RESULTS: All four hypotheses proposed in this study have been proved to be held. Clinicians' job satisfaction has played a mediating role in the impact of slack resources and its three dimensions on individual performance. Among them, there has been a complete mediating role for staff slack, while time and space dimensions have played a partial mediating role in the impact of slack resources on individual performance. CONCLUSIONS: In public hospitals in environments where behavior is subject to significant government interference, it is necessary and feasible to retain appropriate slack resources to improve individual performance. From the perspective of resources management in hospitals, it is necessary for public hospitals to implement a strategy of reserving an appropriate portion of time, staff and space in order to have the conditions to improve clinicians' satisfaction. The existence of slack resources in public hospitals can improve the job satisfaction of clinicians, and then improve the individual performance through the process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".