Shaping the future of primary care in Canada: trainee insights on patient and public engagement in health system transformation research
Bibliographic record
Abstract
Access to healthcare in Canada remains a significant issue, with over one in five people lacking attachment to a regular primary care provider. To address this, patients, health professionals, researchers and policymakers are advocating for health system transformation aimed at improving access and achieving the quintuple aim. As a result, research funding increasingly prioritizes health system transformation. However, whilst collaborative approaches such as integrated knowledge translation (IKT) are critical for success, trainees face barriers to integrating patient and public engagement into their research. These challenges include limited time and resources, difficulties in developing meaningful partnerships, tensions between independent intellectual contributions and collaborative research and academic structures that reinforce power imbalances. This commentary presents four trainee experiences that demonstrate successful capacity-building initiatives for trainees to embed patient and public engagement in health system transformation research. The first case study focusses on the Patient Expertise in Research Collaboration (PERC), which supports Transdisciplinary Understanding and Training on Research-Primary Health Care (TUTOR-PHC) trainees in incorporating patient perspectives into primary healthcare research. The second highlights the role of the Integrated Knowledge Translation Research Network (IKTRN) in building trainee capacity through funding. The third explores a trainee experience with the ACCESS Open Minds Network (AOM). The fourth describes a trainee experience within a co-design study, the Enhancing Physical and Community MoBility in OLDEr Adults with Health Inequities Using CommuNity Co-Design (EMBOLDEN). These case studies provide insights into effective strategies for overcoming barriers to patient and public engagement in research. However, opportunities for such engagement remain uneven and depend on limited funding. To foster sustainable support, academic institutions must integrate these capacity-building initiatives, promoting a future of primary care in Canada that is inclusive, patient-centred and responsive to evolving population needs.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.062 | 0.033 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".