Strategic Workforce Planning for Health Human Resources
Bibliographic record
Abstract
Background Health-care organizations provide services in a challenging environment, making the introduction of health human resources initiatives especially critical for safe patient care. Purpose To demonstrate how one specialty hospital in Ontario, Canada, leveraged an employment policy to stabilize its nursing workforce over a six-year period (2007 to 2012). Methods An observational cross-sectional study was conducted in which administrative data were analyzed to compare full-time status and retention of new nurses prepolicy and during the policy. The Professionalism and Environmental Factors in the Workplace Questionnaire® was used to compare new nurses hired into the study hospital with new nurses hired in other health-care settings. Results There was a significant increase in full-time employment and a decrease in part-time employment in the study hospital nursing workforce. On average, 26% of prepolicy new hires left the study hospital within one year of employment compared to 5% of new hires during policy implementation. The hospital nurses scored significantly higher than nurses employed in other health-care settings on 5 out of 13 subscales of professionalism. Conclusions Decision makers can use these findings to develop comprehensive health human resources guidelines and mechanisms that support strategic workforce planning to sustain and strengthen the health-care system.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".