Impacts of patient and family engagement in hospital planning and improvement: qualitative interviews with patient/family advisors and hospital staff
Bibliographic record
Abstract
BACKGROUND: Patient engagement (PE) in hospital planning and improvement is widespread, yet we lack evidence of its impact. We aimed to identify benefits and harms that could be used to assess the impact of hospital PE. METHODS: We interviewed hospital-affiliated persons involved in PE activities using a qualitative descriptive approach and inductive content analysis to derive themes. We interpreted themes by mapping to an existing framework of healthcare performance measures and reported themes with exemplar quotes. RESULTS: Participants included 38 patient/family advisors, PE managers and clinicians from 9 hospitals (2 < 100 beds, 4 100 + beds, 3 teaching). Benefits of PE activities included 9 impacts on the capacity of hospitals. PE activities involved patient/family advisors and clinicians/staff in developing and spreading new PE processes across hospital units or departments, and those involved became more adept and engaged. PE had beneficial effects on hospital structures/resources, clinician staff functions and processes, patient experience and patient outcomes. A total of 14 beneficial impacts of PE were identified across these domains. Few unintended or harmful impacts were identified: overextended patient/family advisors, patient/family advisor turnover and clinician frustration if PE slowed the pace of planning and improvement. CONCLUSIONS: The 23 self reported impacts were captured in a Framework of Impacts of Patient/Family Engagement on Hospital Planning and Improvement, which can be used by decision-makers to assess and allocate resources to hospital PE, and as the basis for ongoing research on the impacts of hospital PE and how to measure it.
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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 | 0.034 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".