Using qualitative research perspectives to inform patient engagement in research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it