Using qualitative research perspectives to inform patient engagement in research
Bibliographic record
Abstract
PLAIN ENGLISH SUMMARY: In Canada, and internationally, there is an increased demand for patient engagement in health care research. Patients are being involved throughout the research process in a variety of roles that extend beyond the traditional passive participant role. These practices, referred to collectively as 'patient engagement', have raised questions about how to engage patients in the research process. Specifically, researchers have noted a lack of theory underpinning patient engagement and are looking for guidance on how to select patients and engage patients throughout the research process. In this commentary, we draw on qualitative research perspectives to generate theoretical and methodological ideas that novice or experienced researchers can apply to facilitate patient engagement in research. ABSTRACT: Despite the recent advancements in patient engagement in health care research, there is limited research evidence regarding the best strategies for developing and supporting research partnerships with patients and caregivers. Three particular outstanding concerns that have been reported in the literature and that we will explore in this commentary are: (i) the lack of theoretical underpinning to inform the practice of patient engagement in research; (ii) the lack of knowledge regarding how to select patients to engage in research; and (iii) the lack of clear guidance about the best methods for engaging patients in research. We draw on qualitative research perspectives to reflect on these three areas of concern and propose insights into the theory and methods that we believe are useful for engaging patients in research.
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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 | 0.202 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".