Higher education curricula and approaches to patient engagement in research: A case study
Bibliographic record
Abstract
Context: Integrating patient engagement in research (PER) into higher education curricula is vital to fostering a patient-centered culture and supporting PER in becoming a staple of primary care research. Objective: To describe experiences with the co-design and co-delivery of a graduate-level course on approaches to PER. Study Design and Analysis: Case study and critical reflection. Setting: University of Manitoba (Winnipeg, Canada). Population: Students enrolled in (n = 4), and patient partners (PPs, n = 5) and instructors (n = 2) co-delivering, the course. Intervention: This 13-week, virtually-delivered course was organized into 3 sections: historical and health research contexts; frameworks and approaches; and impact, evaluation, and future directions. PPs carved out a regular and active role, wherein each 4-week section included two 3-hour seminars attended by instructors and students, a 2-hour guided discussion with pre-assigned student and PP groups, and an optional PP office hour. Assignments were reviewed by instructors and PPs and required students to develop a PE protocol specific to their graduate research, critically reflect upon PE within their research area, and reflect on learnings for each course section. Outcome Measure: Reflections on the course (guided by a questionnaire). Results: All 3 groups revealed that the course effectively conveyed the knowledge, skills, and considerations key to PER and underscored the positive impacts of actively involving PPs throughout the course. Relatedly, a commonly mentioned benefit was not just reading about the importance of relationships to PER but also seeing the importance of relationships unfold through course delivery. Students also appreciated receiving both theoretical and lived experience insights. PPs valued the opportunity to mentor students and apply their experiences to shaping the field of PER, along with learning new skills. Instructors highlighted the synergy that emerged among the groups. Lessons learned included key conversations to have when co-developing and co-delivering this course. Conclusions: To our knowledge, this is the first graduate-level course on PER to engage PPs in its co-design and co-delivery, resulting in experiential and theoretical learning opportunities that synergistically transformed students, PPs, and instructors. This course is an example of patient-centered approaches to shaping the next generation of primary care researchers and clinicians.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".