Studying How Patient Engagement Influences Research: A Mixed Methods Study
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
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.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it