Studying How Patient Engagement Influences Research: A Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: There is evidence supporting the value of patient engagement (PE) in research to patients and researchers. However, there is little research evidence on the influence of PE throughout the entire research process as well as the outcomes of research engagement. The purpose of our study is to add to this evidence. METHODS: We used a convergent mixed method design to guide the integration of our survey data and observation data to assess the influence of PE in two groups, comprising patient research partners (PRPs), clinicians, and researchers. A PRP led one group (PLG) and an academic researcher led the other (RLG). Both groups were given the same research question and tasked to design and conduct an inflammatory bowel disease (IBD)-related patient preference study. We administered validated evaluation tools at three points and observed PE in the two groups conducting the IBD study. RESULTS: PRPs in both groups took on many operational roles and influenced all stages of the IBD-related qualitative study: launch, design, implementation, and knowledge translation. PRPs provided more clarity on the study design, target population, inclusion-exclusion criteria, data collection approach, and the results. PRPs helped operationalize the project question, develop study material and data collection instruments, collect data, and present the data in a relevant and understandable manner to the patient community. The synergy of collaborative partnership resulted in two projects that were patient-centered, meaningful, understandable, legitimate, rigorous, adaptable, feasible, ethical and transparent, timely, and sustainable. CONCLUSION: Collaborative and meaningful engagement of patients and researchers can influence all stages of qualitative research including design and approach, and outputs.
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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 | 0.233 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".