Learning collaborative teamwork: an argument for incorporating the humanities
Bibliographic record
Abstract
A holistic, collaborative interprofessional team approach, which includes patients and families as significant decision-making members, has been proposed to address the increasing burden being placed on the health-care system. This project hypothesized that learning activities related to the humanities during clinical placements could enhance interprofessional teamwork. Through an interprofessional team of faculty, clinical staff, students, and patient representatives, we developed and piloted the self-learning module, "interprofessional education for collaborative person-centred practice through the humanities". The module was designed to provide learners from different professions and educational levels with a clinical placement/residency experience that would enable them, through a lens of the humanities, to better understand interprofessional collaborative person-centred care without structured interprofessional placement activities. Learners reported the self-paced and self-directed module to be a satisfactory learning experience in all four areas of care at our institution, and certain attitudes and knowledge were significantly and positively affected. The module's evaluation resulted in a revised edition providing improved structure and instruction for students with no experience in self-directed learning. The module was recently adapted into an interactive bilingual (French and English) online e-learning module to facilitate its integration into the pre-licensure curriculum at colleges and universities.
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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 | 0.069 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.102 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".