Building capacity for interprofessional practice
Bibliographic record
Abstract
BACKGROUND: Evidence indicates that professional development focused on collaborative practice can improve the quality of care and patient outcomes in specific populations. However, current educational knowledge does not include how to teach professionals to provide interprofessional collaborative care. METHODS: This paper discusses the design, implementation and evaluation of the Interprofessional Collaborative Learning Series (IP-CLS), which provides clinicians with interprofessional professional development that promotes interprofessional competencies, allowing them to incorporate elements of interprofessional collaboration into practice, and creates leaders for interprofessional collaborative practice. The IP-CLS was piloted at a regional health centre in Ontario. Participants completed an online retrospective before and after self-assessment to determine the extent to which the IP-CLS contributed to changes in participants' behaviours related to interprofessional collaboration. A focus group further explored the extent to which the IP-CLS fostered change. RESULTS: Online survey results and an analysis of focus group transcripts reveal the strengths of the IP-CLS and the elements that could be improved upon. Findings indicate that the IP-CLS has the potential to build capacity for interprofessional collaboration. DISCUSSION: The findings indicate that the IP-CLS has the potential to build capacity for interprofessional collaborative practice, and to help participants incorporate elements of interprofessional collaboration into practice.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".