Engaging patients as partners in health research: Lessons from BC, Canada
Bibliographic record
Abstract
Canada is seeing increased interest in engaging patients in health research, recognizing the potential to improve its relevance and quality. The momentum is promising, but there may be a tendency to ignore the challenges inherent when lay people and professionals collaborate. We address some of these challenges as they relate to recruitment, training, and support for patients at the British Columbia (BC) Support for People and Patient-Oriented Research Unit, part of Canada's Strategy for Patient-Oriented Research. A retrospective review of a telehealth project demonstrates that, as well as the practical elements of recruitment, training, and support, attention must be paid to issues of credibility, legitimacy, and power when engaging patients. We propose that all patient-oriented research projects would benefit from using a similar framework to guide patient engagement planning and implementation, helping to anticipate and mitigate challenges from the outset. Projects would ideally also include the study of patient engagement methods, to add to this important body of knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".