Consensus on how to optimise patient/family engagement in hospital planning and improvement: a Delphi survey
Bibliographic record
Abstract
OBJECTIVE: Patient and family engagement (PE) in health service planning and improvement is widely advocated, yet little prior research offered guidance on how to optimise PE, particularly in hospitals. This study aimed to engage stakeholders in generating evidence-informed consensus on recommendations to optimise PE. DESIGN: We transformed PE processes and resources from prior research into recommendations that populated an online Delphi survey. SETTING AND PARTICIPANTS: Panellists included 58 persons with PE experience including: 22 patient/family advisors and 36 others (PE managers, clinicians, executives and researchers) in round 1 (100%) and 55 in round 2 (95%). OUTCOME MEASURES: Ratings of importance on a seven-point Likert scale of 48 strategies organised in domains: engagement approaches, strategies to integrate diverse perspectives, facilitators, strategies to champion engagement and hospital capacity for engagement. RESULTS: Of 50 recommendations, 80% or more of panellists prioritised 32 recommendations (27 in round 1, 5 in round 2) across 5 domains: 5 engagement approaches, 4 strategies to identify and integrate diverse patient/family advisor perspectives, 9 strategies to enable meaningful engagement, 9 strategies by which hospitals can champion PE and 5 elements of hospital capacity considered essential for supporting PE. There was high congruence in rating between patient/family advisors and healthcare professionals for all but six recommendations that were highly rated by patient/family advisors but not by others: capturing diverse perspectives, including a critical volume of advisors on committees/teams, prospectively monitoring PE, advocating for government funding of PE, including PE in healthcare worker job descriptions and sharing PE strategies across hospitals. CONCLUSIONS: Decision-makers (eg, health system policy-makers, hospitals executives and managers) can use these recommendations as a framework by which to plan and operationalise PE, or evaluate and improve PE in their own settings. Ongoing research is needed to monitor the uptake and impact of these recommendations on PE policy and practice.
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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 | 0.144 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".