Key ingredients for successful collaboration in health research: perspectives of patient research partners
Bibliographic record
Abstract
BACKGROUND: There are increasing publications on meaningful collaboration between researchers and patient research partners (PRPs), but fewer publications of such work from the PRP perspective using an evaluation framework. Our aim is to present our own perspectives and reflections on meaningful collaboration as PRPs working on a qualitative research study. MAIN BODY: We were part of a study team that comprised of PRPs, clinicians and academic researchers, and was led by a PRP. The team designed and conducted a qualitative study aimed at understanding how patients make decisions around tapering of biologics for inflammatory bowel disease. The study was conducted online. The PRP lead was trained in qualitative methodology through a one-year certificate program called Patient and Community Engagement Research offered through the University of Calgary Continuing Education. We had received patient-oriented research training and qualitative research training prior to this project. Team members were assigned tasks by our group lead based on member interests and willingness. Some group members were part of the Strategy for Patient-Oriented Research, Inflammation, Microbiome, and Alimentation: Gastro-Intestinal and Neuropsychiatric Effects Network, one of five chronic disease networks in the Strategy for Patient Oriented Research initiative of the Canadian Institutes of Health Research. We describe the five key ingredients to successful collaboration based on our experiences and reflections utilizing the Experience-Reflection-Action Cycle as our framework. The five key ingredients that we identified were: inclusiveness, goal and role clarity, multi-level training and capacity building, shared decision making, and a supportive team lead. CONCLUSION: Overall, our experience was positive. With successful collaboration came an increased level of trust, commitment and performance. There is a need for more studies with diverse PRPs in different settings to validate and/or identify additional factors to improve collaboration in patient-oriented research.
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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 | 0.198 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.012 | 0.027 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".