Co-developing patient and family engagement indicators for health system improvement with healthcare system stakeholders: a consensus study
Bibliographic record
Abstract
OBJECTIVE: To develop a set of patient and family engagement indicators (PFE-Is) for measuring engagement in health system improvement for a Canadian provincial health delivery system through an evidence-based consensus approach. DESIGN: This mixed-method, multiphase project included: (1) identification of existing measures of patient and family engagement through a review of the literature and consultations with a diverse provincial council of patients, caregivers, community members and researchers. The Public and Patient Engagement Evaluation Tool (PPEET) was selected; (2) consultations on relevance, acceptability and importance with patient and family advisors, and staff members of Alberta Health Services' Strategic Clinical Networks. This phase included surveys and one-on-one semi-structured interviews aimed to further explore the use of PPEET in this context. Findings from the survey and interviews informed the development of PFE-Is; (3) a Delphi consensus process using a modified RAND/UCLA Appropriateness Method to identify and refine a core set of PFE-Is. PARTICIPANTS: The consensus panel consisted of patients, family members, community representatives, clinicians, researchers and healthcare leadership. RESULTS: From an initial list of 33 evidence-based PFE-Is identified, the consensus process yielded 18 final indicators. These PFE-Is were grouped into seven themes: communication, comfort to contribute, support needed for engagement, impact and influence of engagement initiative, diversity of perspectives, respectful engagement, and working together indicators. CONCLUSIONS: This group of final patient, family and health system leaders informed indicators can be used to measure and evaluate meaningful engagement in health research and system transformation. The use of these metrics can help to improve the quality of patient and family engagement to drive health research and system transformation.
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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 | 0.299 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".