The ARCTIC Workshop: An Interprofessional Education Activity in an Academic Health Sciences Center
Bibliographic record
Abstract
The complex care required to address the needs of head and neck cancer patients requires interprofessional collaboration. Using the compelling narrative of a patient's journey through cancer treatment in the Canadian setting, the aim of this study was to engage health professions students to discover the importance of interprofessional care for complex patients, while delivering content on head and neck cancer care and providing training/experience in interprofessional education (IPE) facilitation to clinicians. In the study, 38 students from nine health disciplines participated in a three-hour workshop that included interactive presentations and facilitated small- and large-group activities. The Interdisciplinary Education Perception Scale (IEPS) was administered pre and post workshop to examine changes in students' attitudes and perceptions about IPE. Qualitative participant and facilitator feedback regarding the session was obtained using a structured questionnaire and debriefing sessions with each group. An overall improvement of scores on the IEPS was observed, while analyses of individual items showed improved scores on all items but one. Session feedback from students and facilitators was positive. The results suggest that combining case-based methods with interprofessional learning in the clinical setting allowed students to develop an appreciation for the complex needs of head and neck cancer patients and the need for collaboration to improve patient outcomes.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".