Exploring conceptualizations of impact from the perspectives of patient partners working with Canadian health system organizations: a qualitative study
Bibliographic record
Abstract
Introduction: As the patient partner role, described as the longitudinal and bi-directional involvement of patients, becomes increasingly integrated into health system organizations, it is imperative to understand the impacts of this engagement. The objective of this study is to explore how patient partners conceptualize and aspire to achieve impact through their partnership work, as well as to identify factors that may help to facilitate these impacts. Methods: Guided by Interpretive Description methodology, we conducted 35 semi-structured interviews with patient partners working with health system organizations across Canada. Eligible participants self-identified as a patient, family member, or caregiver with over two years of experience as a patient partner. Participants were recruited through a previous survey, newsletter advertisements, and the research team’s network. Data collection and analysis occurred concurrently in an iterative process and involved staged coding as well as the development of a thematic template consisting of themes and sub-themes related to conceptualizations of impact, aspirations of impact, and factors that facilitate impact. Results: Participants described the impacts of their involvement in three main ways; observable versus perceived impacts, aspirational versus expected impacts, and factors that help to facilitate impact. Observable impacts involved extrinsic concrete changes, while perceived impacts involved a sense of intrinsic validation. Aspirational impacts encompassed changes participants hoped to see, and expected impacts were changes participants anticipated to occur. Lastly, participants highlighted multiple organizational supports that they perceived helped to facilitate impact. Conclusion: Patient partners conceptualized their impact in various ways and commonly discussed observable and perceived impacts in relation to their work within their organization, and broader system change as an aspirational impact. Understanding how patient partners describe and aspire to impact can help organizations optimize impactful partnership work. Future research should explore how organizational staff conceptualize impact to compare these perspectives with those of patient partners.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".