"Walking the Walk”: Promoting Competencies for Interprofessional Learning through Team Meetings—A Case Study
Bibliographic record
Abstract
Background: Interprofessional learning is a key aspect of improving team-based healthcare. Core competencies for interprofessional education (IPE) activities have recently been developed, but there is a lack of guidance as to practical application. Methods and Findings: Cancer Forum is a weekly multi-professional meeting used as the case study for this report. Power was identified as a critical issue and six questions were identified as the basis for a structured reflection on the conduct of Cancer Forum. Results were then synthesised using Habermas’ delineation of learning as instrumental, normative, communicative, dramaturgical, and emancipatory. Power was a key issue in identified obstacles to inter professional learning. Leadership emerged as a cross-cutting theme and was added as a seventh question. The emancipatory potential of interprofessional learning benefited from explicit consideration of the meeting agenda to promote competencies of sharing role knowledge, teamwork and communication. Modelling of required skills fulfils a dramaturgical and normative role. Conclusions: The structured reflection tool highlighted the relationship between power and IPE competencies. It was essential to walk the walk as well as talk. The process followed provides a practical guide for using team meetings to promote interprofessional learning competencies and thereby improving patient care.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".