Understanding middle managers’ influence in implementing patient safety culture
Bibliographic record
Abstract
BACKGROUND: The past fifteen years have been marked by large-scale change efforts undertaken by healthcare organizations to improve patient safety and patient-centered care. Despite substantial investment of effort and resources, many of these large-scale or "radical change" initiatives, like those in other industries, have enjoyed limited success - with practice and behavioural changes neither fully adopted nor ultimately sustained - which has in large part been ascribed to inadequate implementation efforts. Culture change to "patient safety culture" (PSC) is among these radical change initiatives, where results to date have been mixed at best. DISCUSSION: This paper responds to calls for research that focus on explicating factors that affect efforts to implement radical change in healthcare contexts, and focuses on PSC as the radical change implementation. Specifically, this paper offers a novel conceptual model based on Organizational Learning Theory to explain the ability of middle managers in healthcare organizations to influence patient safety culture change. We propose that middle managers can capitalize on their unique position between upper and lower levels in the organization and engage in 'ambidextrous' learning that is critical to implementing and sustaining radical change. This organizational learning perspective offers an innovative way of framing the mid-level managers' role, through both explorative and exploitative activities, which further considers the necessary organizational context in which they operate.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".