Patient- and public-driven health research: a model of co-leadership and partnership in research priority setting using a modified James Lind Alliance approach
Bibliographic record
Abstract
OBJECTIVES: To describe the Strategy for Patient-Oriented Research Evidence Alliance's methodological approach to systematically identify 23 high priority health research topics (three in 2021 and 20 in 2023) from patient partners (including caregivers) and members of the public across Canada and beyond. STUDY DESIGN AND SETTING: In 2021 and 2023, we collaborated with patient and public partners to co-design and co-conduct two priority setting initiatives. These initiatives involved a diverse group of patients, the public, clinicians, researchers, and health system decision-makers to systematically and collectively prioritize research topics based on their perceived importance and anticipated impact. We used a modified James Lind Alliance approach, where all participants were engaged as equal partners. The prioritization process consisted of the following steps: 1) identification and collection of research priorities from patients and the public; 2) summarizing the research priorities gathered; 3) conducting semistructured interviews (1-on-1 or focus groups depending on the number of submissions for each unique topic), conducting literature searches on each topic to identify relevant knowledge synthesis and appraising the quality of relevant evidence using the AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) checklist, and preparing lay summaries (1-2 pages) for each unique topic using a predefined template cocreated with patient partners; 4) conducting a priority setting exercise with a multidisciplinary panel consisting of an interim priority setting rating questionnaire to score each topic based on nine questions, followed by a virtual workshop to reach consensus on the final rating and ranking of topics; and 5) facilitating research by funding selected topics and providing capacity-building support to research teams. We conducted a formal process evaluation of engagement, transparency, information management, and considerations of values and context in 2023. RESULTS: A total of 98 topics were received across two research priority setting initiatives. Approximately, half the submissions were made by individuals who identified as patients (2021: 45% [n = 5] and 2023: 52% [n = 45]), whereas the rest identified as caregivers or members of the public. Topics were spread across 26 health themes, with arthritis and osteoporosis (27% [n = 3]) being the most common theme in 2021 and quality of care (26% [n = 23]) in 2023. Twenty-three priorities from 98 topics submitted by patients and public were selected. A formal process evaluation in 2023 revealed 85% of the respondents who participated in the priority setting panel "strongly agreed" that their experience was valuable and they would participate again in a future initiative. The 23 prioritized projects are currently being co-led with the patient and public partner topic submitters and nominated research teams. CONCLUSION: Priority setting exercises successfully engaged a diverse group of interested parties, resulting in the identification of relevant and impactful research topics. The positive feedback from participants suggests that these exercises were well-received and that similar methodologies should be applied and refined in future efforts. PLAIN LANGUAGE SUMMARY: The Strategy for Patient-Oriented Research Evidence Alliance used a patient- and public-driven approach to set research priorities across different topics. They conducted two priority setting initiatives in 2021 and 2023, involving patients, the public, researchers, clinicians, and health system managers. This approach aimed to prioritize research topics based on their perceived importance and anticipated impact. A total of 98 topics were received across two research priority setting initiatives, with nearly half the submissions from patients and the other half from caregivers or members of the public. The topics covered 26 health themes, with arthritis and osteoporosis (2021) and quality of care (2023) being the most common themes. From these topics, the 23 highest priority topics were selected by a multidisciplinary priority setting panel. A formal process evaluation in 2023 revealed that 85% of the respondents who participated in the priority setting panel "strongly agreed" that their experience was valuable, and they would participate again in a future initiative. In conclusion, the priority setting exercises successfully engaged a diverse group of individuals and identified important research topics. The positive feedback suggests that this approach was well received and should be applied in future efforts.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".