Skills integration in a simulated and interprofessional environment: An innovative undergraduate applied health curriculum
Bibliographic record
Abstract
The objective of our study was to propose an innovative applied health undergraduate curriculum model that uses simulation and interprofessional education to facilitate students' integration of both technical and "humanistic" core skills. The model incorporates assessment of student readiness for clinical education and readiness for professional practice in a collaborative, team-based, patient-centred environment. Improving the education of health care professionals is a critical contributor to ultimately improving patient care and outcomes. A review of the current models in health sciences education reveals a scarcity of clinical placements, concerns over students' preparedness for clinical education, and profession-specific delivery of health care education which fundamentally lacks collaboration and communication amongst professions. These educational shortcomings ultimately impact the delivery and efficacy of health care. Construct validation of clinical readiness will continue through primary research at The Michener Institute for Applied Health Sciences. As the new educational model is implemented, its impact will be assessed and documented using specific outcomes measurements. Appropriate modifications to the model will be made to ensure improvement and further applicability to an undergraduate medical curriculum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".