The impact of patient engagement on trials and trialists in Ontario, Canada: An interview study with IMPACT awardees
Bibliographic record
Abstract
Abstract Background A key component of patient-oriented research is the engagement of patients as partners in the design and conduct of health research. While there is now national infrastructure and networks to support the engagement of patients as partners, there remain calls for promising practices and success stories. In particular, there remains a keen interest in evaluating the impact that patient engagement has on health research studies. We aimed to investigate the impact that patient engagement had on health research conducted in Ontario, Canada. Methods Our sampling frame was studies that were awarded funding by the Ontario SPOR SUPPORT Unit. Semi-structured interviews were conducted with 10 principal investigators, members of research teams, and patient partners. Interviews explored the role of patient partners, the perceived impact of the patient engagement on the study, challenges faced, and advice for other researchers considering patient engagement. Data were analysed using the thematic analysis method with transcripts coded independently by two members of the study team. All coding and subsequent theme generation were discussed until consensus was achieved. Results There was variation in the methods used to engage patients and other stakeholders, the roles that patients and stakeholders occupied, and where they had input. Interviewees discussed two major areas of impact of patient engagement on research: impact on the study about which they were being interviewed, which tended to relate to improved relevancy of the research to the study population, and impact on themselves which led to changes in their own practice or approaches to future research. Identified challenges to patient engagement included: identifying and reaching patient advisors or patient partners, time-related challenges, and maintaining engagement over the course of the research. Conclusions There remains a need to further build out the concept of relevancy and how it may be operationalised in practice. Further, the longer-term impacts of patient engagement on researchers and research teams remains under-explored and may reveal additional elements for evaluation. Challenges to patient engagement remain, including identifying and maintaining engagement with partners that reflect the diversity of the population of interest.
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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 | 0.049 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".