Meaningful moves: A meaning-based view of nurses’ turnover
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it