Effects of a Longitudinal Interprofessional Educational Outreach Program on Collaboration
Bibliographic record
Abstract
INTRODUCTION: Interprofessional education (IPE) interventions lack clarity regarding development and implementation, impeding a clear understanding of their role and effectiveness. The aim of this study was to identify whether and how an outreach program targeting interprofessional health care teams can improve self-efficacy and interprofessional collaboration (IPC). METHODS: A cohort study was conducted to explore the effect of the program on individual self-efficacy and perceived IPC and investigate factors affecting interprofessional learning and collaboration. The program was a two-year IPE program consisting of workshops, educational materials, and interworkshop support. Participants were physicians, nurses, dietitians, pharmacists, and social workers at two primary care teams in Toronto. Self-efficacy and team function were measured five times throughout the program. We used analysis of variance and t-tests to compare between teams and used Pearson correlations to estimate the relationship between self-efficacy and team function. One-on-one interviews investigated factors affecting IPC and the program's effect on IPC. RESULTS: Team function improved as the program progressed (P = .02); although it did not affect self-efficacy, there was an increasing correlation between self-efficacy and team function as the program progressed (P < .01 for workshop 5). Interviews revealed that trust, liability concerns, and geographic proximity were mediators of IPC. The workshops were perceived to enable trust building by increasing knowledge and allowing nonphysician team members to showcase their expertise. DISCUSSION: Our findings demonstrate that an IPE workshop, through role clarification, cultivation of trust, and a community of practice, can promote these elements. Trust in team members and geographic proximity are potential facilitators to IPC developed during an interprofessional program.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".