Intention to leave among health care professionals: The importance of working conditions and social capital
Bibliographic record
Abstract
Hospitals in Sweden are redesigning their care processes to increase efficiency. However, related to these changes, there is a risk of increased staff intention to leave and turnover due to increased workload and work pace. The literature on work engagement and job demands and resources suggests that specific job resources can buffer negative effects; i.e., intention to leave because of job demands. Social capital is suggested to have the potential to be a resource associated with staff intention to leave. The aim of this study was to investigate the associations between social capital and intention to leave and to test if social capital moderates the relationship between job demands and intention to leave. A sample of five hospitals working under conditions of improvements of care processes were studied using a questionnaire administered to the healthcare clinicians (n = 849). High levels of social capital were associated with low levels of intention to leave. However, the moderating effect of social capital was not confirmed. Intention to leave among occupational groups was influenced differently by social capital, other job resources, and job demands.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".