A Virtual, Multi-Session Workshop Model for Integrating Patient and Public Perspectives in Research Analysis and Interpretation
Bibliographic record
Abstract
The importance and value of engaging patients and the public as co-researchers (i.e., “patient engagement in research”) is becoming more evident, and guiding methods must be available for researchers conducting their work at different points along the engagement spectrum. This article provides a virtual workshop model for integrating patient and public stakeholder perspectives in data analysis and interpretation. The model is based upon a critical reflection on the methods that underlaid the consultation stage of our scoping review on patient and caregiver preferences for cardiac surgery. It involves four virtual workshop sessions held on separate days, each achieving the unique goals of (a) establishing participants’ technological literacy within the virtual platform, (b) obtaining responses to the research question, (c) introducing participant perspectives into research analysis and interpretation, and (d) prioritizing research findings or future research agendas. Further, a description of the considerations related to virtual engagement, including those pertaining to equity, diversity, and inclusion; features of the virtual platform; and roles for the research team are provided. This paper contributes toward a methodological toolkit for patient engagement in research, especially as an adjunct to research with otherwise minimal patient engagement. It also adds to the emerging literature on practical approaches to patient engagement in research as more engagement is occurring virtually.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.094 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".