Investigating a patient-led conference: What are the characteristics and impacts of patient leadership? A qualitative study
Bibliographic record
Abstract
Abstract Background Patient engagement has been implemented in various settings including clinical, research, and quality improvement, with varying levels of patient contributions and decision-making responsibility. However, little is known about the experiences of patient partners who are in leadership roles in patient-led events. For Patients, By Patients (PxP) is an annual, virtual, patient-led conference that focuses on topics important to patient partners in research. Each year’s PxP steering committee is comprised of those with patient experiences and consequently, offers an opportunity for our research team to explore patient leadership within a conference setting. Understanding more about the intricacies of patient-led events is necessary if we wish to support patient leadership as a valuable form of patient engagement. Objectives The aim of this study was to address the current knowledge gap in patient-led events and patient leadership. Design We conducted a qualitative descriptive study of semi-structured virtual interviews with PxP conference steering committee members. Thematic analysis was used to identify core themes that were salient to the data. Setting International virtual setting via Zoom from Jan 2025-April 2025. Participants Purposive sampling was used to conduct interviews with thirteen PxP patient partner steering committee members. Results Four core themes were identified in the data: institutional support, steering committee environmental characteristics, personal growth, and new possibilities. Conclusions Patient-led events offer an opportunity to promote patient leadership. To facilitate patient leadership in this setting, several factors are important including attention to power dynamics, institutional support, and considerations for accessibility and intersectionality.
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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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".