Engaging critically: exploring the varying roles of lived experience advisors in an implementation science study on management of opioid prescribing
Bibliographic record
Abstract
Involvement of individuals with lived experience, also called "patient partners", is a key element within implementation science, the study of how to put evidence into practice. While conducting a 4-year implementation study focused on improving physician management of opioid prescribing, our research team worked closely with Lived Experience Advisors (LEAs). LEAs were involved throughout the study, including developing patient-facing recruitment material, informing the analysis of results, and as a regular reminder of the real-world impact of this work. However, through regular critical reflection, we acknowledged that we were still uncertain how to articulate the impact of LEA involvement. As a team, we continually discussed why and how people with lived experience were involved in this study. We probed ill-defined concepts such as "patient perspective", which was particularly complex for a study focused on changing physician behaviour with indirect impact on patients. This critical reflection strengthened trust and rapport between team members (characteristics deemed essential to meaningful patient involvement), while underscoring the value of including concerted time to explore the muddier aspects of engagement. In short, patient engagement did not proceed as smoothly as planned. We advocate that "best practices" in the engagement of people with lived experience include regularly setting aside time outside of practical study tasks to interrogate complex aspects of patient engagement, including reflecting on how and why individuals with lived experience are involved.
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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 | 0.080 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.011 |
| 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".