Accreditation as a driver of interprofessional education: the Canadian experience
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to (1) explore evidence provided by Canadian health and social care (HASC) academic programs in meeting their profession-specific interprofessional education (IPE)-relevant accreditation standards; (2) share successes, exemplars, and challenges experienced by HASC academic programs in meeting their IPE-relevant accreditation standards; and (3) articulate the impacts of IPE-relevant accreditation standards on enabling interprofessional learning to the global HASC academic community. METHODS: Profession-specific (bilingual, if requested) surveys were developed and emailed to the Deans/Academic Program Directors of eligible academic programs with a request to forward to the individual who oversees IPE accreditation. Responses were collated collectively and by profession. Open-ended responses associated with our first objective were deductively categorized to align with the five Accreditation of Interprofessional Health Education (AIPHE) standards domains. Responses to our additional questions associated with our second and third objectives were inductively categorized into themes. RESULTS/DISCUSSION: Of the 270 HASC academic programs surveyed, 30% (n = 24) partially or completely responded to our questions. Of the 106 IPE-relevant standards where evidence was provided, 62% (n = 66) focused on the Educational Program, 88% of which (n = 58) were either met or partially met, and 47% (n = 31) of which focused on practice-based IPE. Respondents cited various exemplars and challenges in meeting IPE-relevant standards. CONCLUSIONS: The overall sentiment was that IPE accreditation was a significant driver of the IPE curriculum and its continuous improvement. The array of exemplars described in this paper may be of relevance in advancing IPE implementation and accreditation across Canada and perhaps, more importantly, in countries where these processes are yet emerging.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".