Back

Investigating a patient-led conference: What are the characteristics and impacts of patient leadership? A qualitative study.

Magel, T.; Strain, K.; Samson, A.; Richards, D. P.; Mulhall, H.; Khan, K. M.; Lingard, L.

2025-11-09 public and global health
10.1101/2025.11.07.25339430 medRxiv
Show abstract

BackgroundPatient 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 years 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. ObjectivesThe aim of this study was to address the current knowledge gap in patient-led events and patient leadership. DesignWe 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. SettingInternational virtual setting via Zoom from Jan 2025-April 2025. ParticipantsPurposive sampling was used to conduct interviews with thirteen PxP patient partner steering committee members. ResultsFour core themes were identified in the data: institutional support, steering committee environmental characteristics, personal growth, and new possibilities. ConclusionsPatient-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.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.