Human Resource Management Strategies and the Retention of Older RNs
Bibliographic record
Abstract
This study investigated the human resource management strategies that are most important in retaining older RNs in the workforce and the extent to which hospitals are currently engaging in these practices. The participants (n=361) were RNs aged 50 and over employed in hospitals across Ontario. Along with flexible work schedules, the human resource practices rated as most important in the decision of these RNs to remain in the workforce involved compensation (improving benefits; offering incentives for continued employment), recognition and respect (showing appreciation for a job well done; recognizing the experience, knowledge, skill and expertise of older nurses; ensuring that older nurses are treated with respect by others in the organization) and pre- and post-retirement arrangements (retirement with callback; partial or phased retirement options). There were significant differences between the importance that RNs attributed to the 34 human resource practices and the extent to which their hospitals are currently engaged in each practice, with the largest discrepancies occurring for those practices that RNs indicated were most important in their decision to remain in the workforce. While some hospitals may have difficulty in implementing strategies that have budgetary implications, all could implement recognition and respect with minimal financial consequences.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".