Students' experience of the Health Care Team Challenge™: long-term case competition can improve students' competence in interprofessional collaboration.
Bibliographic record
Abstract
In Ontario, recent policy documents have considered interprofessional (IP) care as one of the cornerstones of the health care system.1,2 Nevertheless, many healthcare workers lack awareness about how to work effectively in IP teams, and hence fail to fully capitalize on its benefits.3 To improve competency in collaboration, Canadian health sciences programs have widely introduced IP education as part of their curricula. One IP education strategy is the Health Care Team Challenge (HCTC), a multidisciplinary case competition.4,5 Recent reports have identified key characteristics from an organizer’s point of view,6,7 and have shown statistically significant improvement in students’ attitudes towards IP collaboration.7 Nevertheless, there has been little formal feedback from the participants’ perspective. Unanswered questions include the process of team development and learning for students, and whether the HCTC influences students’ attitudes towards IP collaboration.6 As a group of professional trainees and a faculty mentor (with backgrounds in medicine, nursing, physical therapy and occupational therapy), we report on our experience working as an IP team during the 2011–2012 regional and national Canadian HCTC competitions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".