Will They Stay or Will They Go? Putting Theory into Practice to Guide Effective Workforce Retention Mechanisms
Bibliographic record
Abstract
Policy makers in healthcare in all countries are faced with challenges of designing and implementing strategies that will achieve three major and essential goals: produce enough health workers for a cost-effective skills mix to deliver high-quality care; attract trained health workers into the workforce; and deploy health workers where they are most needed and keep them there. Yet these apparently straightforward strategies are seldom wholly successful, and there is little clear evidence to guide the frustrated policy maker. This paper explores the reasons why it may be so difficult to come up with strategies that guarantee success and looks at what we do know about attracting, retaining and motivating health workers to get them and keep them working productively where they are most needed.
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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 | 0.117 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".