Engaging Patient and Family Advisors in Health-Care System Planning: Experiences and Recommendations
Bibliographic record
Abstract
BACKGROUND: Patient and family advisors (PFAs) contributed to the development of the Ontario Cancer Plan IV (OCP IV), a 4-year strategic plan for Ontario, Canada's cancer system produced by Cancer Care Ontario. OBJECTIVE: To understand the barriers and facilitators PFAs experience when they are engaged in health-care system planning and provide recommendations for future engagement. METHOD: Patient and family advisors who had an ongoing involvement in the development of the OCP IV were invited to take part in an interview. Qualitative data were analyzed for emergent themes and recommendations were generated. RESULTS: Key emergent themes highlighted necessary elements for effective engagement of PFAs. These included rapport (feeling valued, included as an equal and having supportive interpersonal relationships), communication (clarity and transparency, shared language and understanding, feeling heard, and effective teleconferencing), and leadership (from PFAs and staff). Recommendations for optimizing PFA engagement in health-care system planning were generated. CONCLUSION: Patient and family advisors can be effectively engaged in system-level strategic planning by building reciprocal rapport, effective communication, and strong leadership. Notably, developing "systems literacy" in PFAs is key to ensuring the voices of patients and their families are heard and reflected in health-care system plans.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".