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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".