Can interprofessional collaboration provide health human resources solutions? A knowledge synthesis
Bibliographic record
Abstract
Many studies examine the impact of interprofessional (IP) interventions on various health practice and education outcomes. One significant gap is the lack of research on the effects of IP interventions on health human resource (HHR) outcomes. This project synthesized the literature on the impact of IP interventions at the pre- and post-licensure levels on quality workplace, staff satisfaction, recruitment, retention, turnover, choice of employment and cost effectiveness. Forty-one peer-reviewed articles and five IECPCP project reports were included in the review. We found that IP interventions at the post-licensure level improved provider satisfaction and workplace quality. Including IP learning opportunities into practice education in rural communities or in less popular healthcare specialties attracted a higher number of students and therefore may increase employment rates. This area requires more high quality studies to firmly establish the effectiveness of IP interventions in recruiting and retaining future healthcare professionals. There is strong evidence that IP interventions at the post-licensure level reduced patient care costs. The knowledge synthesis has enhanced our understanding of the relationships between IP interventions, IP collaboration and HHR outcomes. Gaps remain in the knowledge of staff retention and determination of staffing costs associated with IP interventions vis-à-vis patient care costs. None of the studies reported long-term data on graduate employment choice, which is essential to fully establish the effectiveness of IP interventions as a HHR recruitment strategy.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".