Optimizing Patient-Partner Engagement and Integration in Research: Recommendations From a Qualitative Study of Focus Groups With Patient-Partners Living With Type 1 or Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: Diabetes is a global health emergency, affecting more than 420 million people worldwide, but care gaps persist. Patient engagement (PE) in research may address these gaps, but meaningful engagement with representative populations of diverse race, language, and socioeconomic status is lacking. Power imbalances and tokenistic engagement are common. Our aim in this study was to understand what diverse patient partners (PPs) who live with diabetes need to participate in research and how to integrate and engage them meaningfully. METHODS: A purposive sampling strategy was used to recruit PPs from the Diabetes Action Canada (DAC) research network. A semistructured focus group guide was used to conduct focus group sessions. Sessions were audio-recorded and transcribed verbatim. Data analysis and synthesis involved Braun and Clarke's reflexive thematic analysis to develop patterned meanings across the data set. RESULTS: Thirty-five PPs participated in 13 focus groups (9 English, 4 French). PPs had a mean age of 55 years, were living with type 2 (46%) or type 1 (37%) diabetes, identified as female/woman (77%), and resided in Ontario (47%) or Québec (40%); 34% identified as racialized. Themes were developed across 3 broad domains: 1) enablers; 2) opportunities for improvement (challenges and recommendations); and 3) diabetes-specific considerations, highlighting distinct challenges and enablers related to participants' lived experiences with diabetes. We used these data to codesign research-stage-specific recommendations to optimize patient integration and engagement in research. CONCLUSIONS: Our focus group study identified enablers and opportunities for improvement to integrate PP perspectives into the DAC research network more meaningfully.
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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 | 0.363 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".