Envisioning roles for patient partners in primary health care systems of the future: A structured priority setting exercise
Bibliographic record
Abstract
Context Primary health care (PHC) is a whole-society approach to health and well-being, providing holistic care to patients across their lifespan. Engaging patients as partners in PHC research is increasingly common and can include developing research questions, acting in governance roles, helping to set research priorities, preforming parts of the research and/or helping to share results. Patient partners can also be engaged in health system decision-making and policymaking to ensure that care delivery and policies are aligned with patient priorities and reflective of patient values. PHC providers routinely consider patients’ perspectives to make choices about their care at an individual level but patients also have valuable expertise that can improve how PHC is organized at practice, regional, and provincial levels. Objective: To engage the PHC community and identify how patient partners can best support primary health care design, delivery, research, and education. Study Design and Analysis The study used a structured participatory mixed methods design, including qualitative individual and group processes with multivariate statistics. A 3-stage concept mapping exercise was used to brainstorm, sort, and rate ideas on ways for patient partners to make meaningful contributions to PHC. A patient advisory board was engaged to plan and implement the study; the advisory group interpreted the brainstormed ideas to create clusters, labels, and go-zone statements (i.e., those that were both relevant and feasible). Setting: Canada Population Studied PHC patients, care partners, researchers, providers, policymakers, and trainees participated in each stage of the mapping exercise (N=45-52/stage). Instrument: Concept mapping software (Group Wisdom) was used to support the 3-stage concept mapping process. Results: Most important and feasible ideas included: awareness-raising about the role and value of patient expertise among clinicians and researchers, advocating for a shared health record, and advocating for meaningful partnerships that foster inclusivity and promote diverse voices. Ideas around integrating patients as partners in PHC practice settings, policy and governance were rated as important but more challenging to enact. Conclusions Many opportunities exist to integrate patient partners’ valuable expertise to support PHC transformation across research, practice, policy, and education, which can contribute to a PHC system informed by patients’ priorities.
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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 | 0.124 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".