Working together in health research: a mixed-methods patient engagement evaluation
Bibliographic record
Abstract
BACKGROUND: In patient-oriented research (POR), patients contribute their valuable knowledge and lived-experiences to work together as active research partners at all stages of the health research cycle. However, research looking to understand how patient research partners (PRPs) and researchers work together in meaningful and collaborative ways remains limited. This study aims to evaluate patient engagement with the RePORT Patient Advisory Council (PAC) and to identify barriers and facilitators to meaningful patient engagement encountered within research partnerships involving patient research partners and researchers. METHODS: The RePORT PAC members included nine PRPs and nine researchers (clinician-researchers, research staff, patient engagement experts) from both Alberta and British Columbia. All members were contacted and invited to complete an anonymous online survey (Public and Patient Engagement Evaluation (PPEET) tool) at two different project times points. The PAC was invited for a semi-structured interview to gain in-depth understanding of their experiences working together. Interviews were audio-recorded, transcribed, and the data was thematically analyzed with the support of a qualitative analysis software, NVivo. RESULTS: A total of nine PRPs (100%) and three researchers (33%) participated in the baseline survey in February 2022 while six PRPs (67%) responded and three researchers (33%) completed the follow up survey in May 2022. For the semi-structured interviews, nine PRPs (100%) and six researchers (67%) participated. According to the survey results, PAC members agreed that the supports (e. g. training, compensation) needed to contribute to the project were available throughout the project. The survey responses also showed that most members of the PAC felt their opinions and views were heard. Responses to the survey regarding diversity within the PAC were mixed. There were many suggestions for improving diversity and collaboration provided by PAC members during the semi-structured interviews. PAC members mentioned that PAC PRPs informed the co-development of research materials such as recruitment posters and interview guides for the RePORT study. CONCLUSIONS: Through fostering a collaborative environment, we can engage a diverse group of people to work together meaningfully in health research. We have identified what works well, and areas for improvement within our research partnership involving PRPs and researchers as well as recommendations for POR projects more broadly, going forward.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.229 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".