The State of Patient Engagement among Pain Research Trainees in Canada: Results of a National Web-Based Survey
Bibliographic record
Abstract
Patient engagement (PE) in research refers to partnering with people with lived experience (e.g., patients, caregivers, family) as collaborators in the research process. Although PE is increasingly being recognized as an important aspect of health research, the current state of PE among pain research trainees in Canada is unclear. The aims of this study were to describe perspectives about and experiences with PE among trainees conducting pain research in Canada, to identify perceived barriers and facilitators, and to describe recommendations to improve its implementation. A cross-sectional web-based survey (English and French) was administered to trainees at any level conducting pain research at any Canadian academic institution. A total of 128 responses were received; 115 responses were complete and included in the final analysis. The majority of respondents identified as women (90/115; 78.3%), in graduate school (83/115; 72.2%), and conducting clinical pain research (83/115; 72.2%). Most respondents (103/115; 89.6%) indicated that PE is “very” or “extremely” important. Despite this, only a minority of respondents (23/111; 20.7%) indicated that they “often” or “always” implement PE within their own research. The most common barrier identified was lack of knowledge regarding the practical implementation of PE, and understanding its positive value was the most commonly reported facilitator. Recommendations for improving the implementation of PE were diverse. Despite viewing PE as important in research, a minority of pain research trainees regularly implement PE. Results highlight perceived barriers and facilitators to PE and provide insight to inform the development of future training and other enabling initiatives.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".