Bibliographic record
Abstract
Interprofessional education (IPE) within health care programs has been shown to improve patient care and satisfaction, reduce clinical error rates, improve collaborative team behaviour, and diminish negative professional stereotypes. In recognizing this need for IPE as well as the universal commonality and interest in gross anatomy, an IP cadaveric dissection course was instituted at McMaster University in 2008. For the last 5 years this ten week Problem Based Learning (PBL)‐based gross anatomy course involving cadaver dissection in IP teams was offered to students in medicine, midwifery, nursing, physician assistant, occupational therapy and physiotherapy. Of 100–140 interested students twenty‐eight were randomly selected and allocated into 4 IP groups, consisting of 4–6 health professions each. Pre‐experience and post‐experience surveys consisting of the revised Interdisciplinary Education Perception Scale (IEPS) and revised Readiness for Interprofessional Learning Scale (RIPLS) were used to measure differences in attitudes and perceptions towards interprofessional education and collaboration, while qualitative analysis were used to evaluate these changes. Even though the students volunteered because of interest in an IP event, significant improvements were seen in the RIPL Subscale “Positive Professional Identity” and the IEP Subscale “Competency & Autonomy” and qualitative analysis indicated improvements in role clarity, anatomy knowledge, interpersonal facilitation and IP learning.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".