17 How do hospitals ‘walk the talk?’: exploring the connection between hospital goals and practices
Bibliographic record
Abstract
Background Health service organizations have increasingly adopted mission, vision, and value (MVV) statements, vital for communicating strategic plans and demonstrating organizational goals and values for patient and family engagement (PE). These statements, particularly those promoting patient and family engagement (PE), can significantly influence healthcare practices. Yet, understanding how MVV statements foster PE practices remains unclear. Objectives Explore the link between hospital PE goals and practices. Methods We conducted a scoping review exploring how hospital PE goals encapsulated in MVV statements support PE processes. We further supplemented our review with a qualitative case study of a Canadian hospital system. Results Our findings converge on five strategies for operationalizing hospital PE goals: clear communication of organizational goals; aligning organizational documents and administrative processes with these goals; providing resources and support to staff; and cultivating a culture of motivation and empowerment for staff integration of PE in their work. Conversely, misalignment between hospital goals and practices negatively impacts team dynamics and individual attitudes, underscoring the need for strategic alignment. We identified external regulatory and financial pressures as significant barriers to PE practices. Hospitals serving diverse communities faced additional challenges in developing and communicating PE goals. Emphasizing PE goals in organizational documents and leadership practices spurred PE processes among hospital staff, highlighting the role of strategic alignment in PE implementation. Conclusions Our research offers valuable insights for healthcare organizations in identifying activities and strategies that align goals with practices, ultimately promoting a patient and family-centric healthcare environment. This understanding is crucial for health service organizations aiming to enhance PE and improve health outcomes by leveraging their MVV statements effectively.
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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 | 0.030 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".