Pediatric Patient and Family Advisory Councils: A Guide to Their Development and Ongoing Implementation
Bibliographic record
Abstract
BACKGROUND: Patient and family engagement is increasingly recognized in the care of children with complex health conditions. Through the implementation of Patient and Family Advisory Councils (PFACs), health-care institutions are working to improve patient care by nurturing partnerships among patients/families, managers, and clinicians. Despite the potential for PFACs, empirical research about their implementation remains scarce. OBJECTIVE: To address this gap, this study explored the recruitment, retention, and implementation strategies used by Canadian PFACs. DESIGN: We used a qualitative descriptive design. PARTICIPANTS: We interviewed 10 spokespersons from Canadian PFACs. RESULTS: We found themes within 2 stages of implementation. The first stage, getting PFACs started, included 4 themes: (1) using evolving recruitment methods, (2) preparing for effective participation, (3) ensuring diversity within PFACs, and (4) preparing terms of reference. The second stage involved strategies to support ongoing PFACs implementation and included 1 overall theme: facilitating optimal PFACs participation. The underlying link between themes was that establishing/maintaining PFACs is an ongoing learning curve. CONCLUSION: Our findings have the potential to inform new and existing PFACs.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.023 |
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".