Barriers and Facilitators to the Recruitment and Engagement of Diverse Populations Into Patient and Family Advisory Councils: A Scoping Review
Bibliographic record
Abstract
Patient and family advisory councils (PFACs) serve as structured collaborative groups where patients and caregivers partner with healthcare professionals to shape policies, service delivery, and research. Despite guidelines emphasizing the need for diverse representation, PFACs often remain socio-demographically homogenous, excluding vulnerable populations from critical discussions that shape healthcare outcomes. This scoping review examines barriers and facilitators influencing the recruitment and engagement of diverse populations in PFACs. A systematic search identified studies focusing on recruitment and engagement barriers and facilitators targeting under-represented groups. Forty-three studies that met the inclusion criteria were included in the review. Findings reveal that while race/ethnicity, socioeconomic status, and age are commonly considered diversity factors, other key populations such as individuals with disabilities, migrants, and those with lived experiences of homelessness, are often overlooked. Facilitators that can improve reach to these vulnerable populations include culturally tailored outreach, relationship-building with community leaders, and reducing logistical barriers. This review provides actionable recommendations for improving diversity in PFACs, ensuring equitable patient engagement that reflects the full spectrum of healthcare experiences.
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 | 0.058 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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; 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".