The influence mechanism of psychological contract on primary medical staff's turnover intention in the context of COVID‐19 pandemic in China
Bibliographic record
Abstract
OBJECTIVE: This research aims to study the influence mechanism of psychological contract on the turnover intention of primary medical staff in the context of Corona Virus Disease 2019 (COVID-19) fighting. METHODS: Six hundred and fifteen primary medical staff from 13 primary health care institutions in Jianghan District, Wuhan City, Hubei Province, China were selected by random sampling. Psychological contract, emotional exhaustion and turnover intention questionnaires were adjusted appropriately according to research needs, and 5-point Likert scale was used to measure. RESULTS: Normal, interpersonal, and developmental contracts were negatively associated with turnover intention. Emotional exhaustion mediated the effects of interpersonal and developmental contracts on turnover intention. CONCLUSION: The government should establish a long-term incentive mechanism for primary medical staff, fully recognise the work of them in fighting against COVID-19, pay close attention to the psychological state of them, and carry out timely and effective psychological intervention to alleviate their emotional exhaustion and reduce their turnover intention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".