Strengthening Primary Care Research on Health System Transformation in the Education of Health Professionals through PERC-PHC
Bibliographic record
Abstract
Context Access to primary care in Canada is a pressing concern, with over one in five individuals lacking a regular primary care provider. To address this issue, healthcare partners have advocated for system transformation to enhance access and achieve the quintuple aim. However, current health professions education is not adequately preparing professionals to lead such transformative changes. Existing research primarily focuses on health professionals, with minimal engagement of patient partners. The Patient Expertise in Research Collaboration (PERC) centre, funded by the Ontario Strategy for Patient-Oriented Research SUPPORT Unit, aims to promote and support the meaningful engagement of patient partnership in primary health care (PHC) research including those participating in the Transdisciplinary Understanding and Training on Research (TUTOR-PHC) program. Objective This study describes patient engagement in primary health care (PHC) research related to health system transformation in the education of health professionals, facilitated by the PERC-TUTOR PE Fellowship. Study Design A mixed-methods consultation with experts in health system transformation and health professions education (n=77 survey, n=23 interviews), identified eight elements for a framework on health system transformation in health professions education. Through a year-long embedded learning experience in PE, PERC patient advisors provided strategic advice on the framework to lay the foundation for future research on this topic area. Setting Community Population Studied Educators, patient partners, policymakers, healthcare professionals, and researchers. Instrument: Framework on health system transformation in the education of health professionals. Outcome Measures Realignment of health professionals’ education to include health system transformation and PE. Results Patients indicated that each of the eight previously identified framework elements could benefit from patient expertise. Conclusions Focusing solely on health professionals may overlook crucial insights from patients, for whom health system transformation is intended. The PERC-TUTOR PE Fellowship strengthened the development of a framework on health system transformation in health professions education, particularly highlighting the importance of PE. This foundation sets the stage for future studies on this topic.
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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 | 0.103 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".