Understanding patient partnership in health systems: lessons from the Canadian patient partner survey
Bibliographic record
Abstract
OBJECTIVES: To examine the sociodemographic characteristics, activities, motivations, experiences, skills and challenges of patient partners working across multiple health system settings in Canada. DESIGN: Online cross-sectional survey of self-identified patient partners. SETTING: Patient partners in multiple jurisdictions and health system organisations. PARTICIPANTS: 603 patient partners who had drawn on their experiences with the health system as a patient, family member or informal caregiver to try to improve it in some way, through their involvement in the activities of a group, organisation or government. RESULTS: Survey respondents predominantly identified as female (76.6%), white (84%) and university educated (70.2%) but were a heterogeneous group in the scope (activities and organisations), intensity (number of hours) and longevity (number of years) of their role. Primary motivations for becoming a patient partner were the desire to improve the health system based on either a negative (36.2%) or positive (23.3%) experience. Respondents reported feeling enthusiastic (83.6%), valued (76.9%) and needed (63.3%) always or most of the time; just under half felt they had always or often been adequately compensated in their role. Knowledge of the health system and the organisation they partner with are key skills needed. Two-thirds faced barriers in their role with over half identifying power imbalances. Less than half were able to see how their input was reflected in decisions or changes always or most of the time, and 40.3% had thought about quitting. CONCLUSIONS: This survey is the first of its kind to examine at a population level, the characteristics, experiences and dynamics of a large sample of self-identified patient partners. Patient partners in this sample are a sociodemographically homogenous group, yet heterogeneous in the scope, intensity and longevity of roles. Our findings provide key insights at a critical time, to inform the future of patient partnership in health systems.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".