Incentives for improving human resource outcomes in health care: overview of reviews
Bibliographic record
Abstract
OBJECTIVES: To review the effectiveness of financial and nonfinancial incentives for improving the benefits (recruitment, retention, job satisfaction, absenteeism, turnover, intent to leave) of human resource strategies in health care. METHODS: Overview of 33 reviews published from 2000 to 2012 summarized the effectiveness of incentives for improving human resource outcomes in health care (such as job satisfaction, turnover rates, recruitment, and retention) that met the inclusion criteria and were assessed by at least two research members using the Assessment of Multiple Systematic Reviews quality assessment tool. Of those, 13 reviews met the quality criteria and were included in the overview. Information was extracted on a description of the review, the incentives considered, and their impact on human resource outcomes. The information on the relationship between incentives and outcomes was assessed and synthesized. RESULTS: While financial compensation is the best-recognized approach within an incentives package, there is evidence that health care practitioners respond positively to incentives linked to the quality of the working environments including opportunities for professional development, improved work life balance, interprofessional collaboration, and professional autonomy. There is less evidence that workload factors such as job demand, restructured staffing models, re-engineered work designs, ward practices, employment status, or staff skill mix have an impact on human resource outcomes. CONCLUSIONS: Overall, evidence of effective strategies for improving outcomes is mixed. While financial incentives play a key role in enhancing outcomes, they need to be considered as only one strategy within an incentives package. There is stronger evidence that improving the work place environment and instituting mechanisms for work-life balance need to be part of an overall strategy to improve outcomes for health care practitioners.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".