Exploring how Middle Managers Foster Patient Safety Culture through Distributed Leadership
Bibliographic record
Abstract
The importance of improving patient safety, particularly through the creation of patient safety cultures in hospitals, has been a critical focus for healthcare organizations since the Institute of Medicine released its seminal publication over 15 years ago. However, in spite of evidence demonstrating the value of improved organizational and safety culture to hospitals, a gap remains in creating and sustaining PSCs as well as understanding the processes required to implement PSC-focused strategies. In light of this situation, efforts have been made to identify critical factors that lead to improvement in organizations’ PSC. This conceptual paper focuses on one area in particular, the role of middle management in facilitating PSC. In particular, we suggest that middle managers may play a critical role in affecting PSC at both the unit and organizational level by fostering distributed leadership within their respective units. To this end, this conceptual paper aims to frame the extant literature on these areas to better understand a) the under-conceptualized role of middle managers in shaping and influencing patient safety culture; and b) the role of middle managers in fostering distributed leadership around patient safety. Implications for future research are presented.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".