Meaningful moves: A meaning-based view of nurses’ turnover
Bibliographic record
Abstract
Nurses’ turnover is a major global problem with significant service and cost implications. Although sizeable research inquiries have been made into the antecedents, the dynamics, and the consequences of nurses’ turnover, there is still a lack of fine-grained understanding of the psychological states that reflect the cumulative impact of different antecedents and immediately precede nurses’ intentions to quit either from their unit/organization and/or their profession. This paper introduces and develops a meaning-based view of nurses’ turnover. This perspective distinguishes between meaning in work (based on the nurses’ relationship with their work) and meaning at work (based on the nurses’ relationship with their work environment) and explain the implications of high/low meaning in and at work on nurses’ turnover. This meaning-based view of nurses’ turnover offers nurses, administrators and policy makers a deeper and a more nuanced understanding of turnover and promises more tailored remedies for the turnover problem.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".