Interventions that strengthen the care workforce: a realist synthesis review
Bibliographic record
Abstract
Introduction: Health systems depend on care workers to provide "hands-on" direct care with eating, dressing, and other needs, as well as indirect care with household tasks, meals, and transport. Care workers are in high demand to support growing populations who need help in daily life, yet they often fall outside of health human resource planning. Recruiting, supporting, and retaining the care workforce are urgent priorities for health workforce planners. Methods: This realist synthesis review asks: Which interventions strengthen the care workforce? We systematically identified 7396 peer-reviewed sources and 481 gray literature sources, with 151 included in the review. Results: The sources document a variety of interventions that strengthen the care workforce, with an emphasis on pre-service and ongoing training for care workers. There were ambitious interventions that aimed to support the care workforce on multiple fronts. Conclusion: Policy makers and researchers are encouraged to implement complex interventions that cover multiple factors simultaneously. We recommend focusing on legislative structures, educational oversight, and material working conditions, such as scheduling and pay, as highly promising avenues for strengthening the care workforce across multiple contexts.
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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 | 0.024 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".