Measuring the Impact of Patient Engagement in Health Research: An Exploratory Study Using Multiple Survey Tools
Bibliographic record
Abstract
Background: Studies report various ways in which patients are involved in research design and conduct. Limited studies explore the influence of patient engagement (PE) at each research stage in qualitative research from the perspectives of all stakeholders. Methods: = 4) to design and conduct qualitative research aimed at identifying candidate attributes related to patient preferences for tapering biologic treatments in inflammatory bowel disease. We administered surveys before starting, two months into, and post-project work. The surveys contained items from three PE evaluation tools. We assessed the two groups regarding the influence and impact each stakeholder had during the different research stages. Results: PRPs had a moderate or a great deal of influence on the critical research activities across the research stages. They indicated moderate/very/extremely meaningful engagement and agreed/strongly agreed impact of PE. PRPs helped operationalize the research question; design the study and approach; develop study materials; recruit participants; and collect and interpret the data. Conclusion: The three tools together provide deeper insight into the influence of PE at each research stage. Lessons learnt from this study suggest that PE can impact many aspects of research including the design, process, and approach in the context of qualitative research, increasing the patient-centeredness of the study. More comprehensive validated tools are required that work with a more diverse subject pool and in other contexts.
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.
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 | 0.091 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".