Generation-specific incentives and disincentives for nurses to remain employed in acute care hospitals
Bibliographic record
Abstract
AIM: This is a report on generation-specific incentives and disincentives selected by acute care nurses that promote and discourage them to remain employed in hospitals. BACKGROUND: Recent literature indicates that nurse preferences for strategies to promote their retention may differ across generational cohorts. However, current literature is primarily anecdotal with few studies focused on evidence-based generation-specific nurse retention-promoting strategies. METHODS: Data were gathered from a cross-sectional survey administered to a random sample of 9904 registered nurses working in Alberta and Ontario, Canada. Two survey items asking nurses to identify preferences for incentives to remain employed and disincentives that encourage them to leave employment were included. Survey items were based on information gathered from previous focus groups exploring determinants of nurse retention. RESULTS: There were statistically significant differences in the rates of selection across generations of nurses for eight of 10 incentives to remain employed and for eight of 15 disincentives. All generational cohorts selected the same two incentives most frequently: reasonable workloads and manageable nurse-patient ratios. Two of the three most frequently selected disincentives were the same across generations: inadequate staffing and unmanageable workloads. IMPLICATIONS FOR NURSING MANAGEMENT: Leaders should implement and evaluate strategies that ensure workloads are reasonable and nurse-patient ratios are manageable to promote retention among all generations of nurses in the acute care hospital workforce.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".