The shaky terrain of patient quality and safety: the potential of leadership to improve patient and staff experiences
Bibliographic record
Abstract
PURPOSE: Patient safety's inconsistent progress remains a prominent concern. Influential advocates recently identified faltering leadership in stalled initiatives. This study aims to identify how patient care quality and safety can be improved by analyzing leadership and organizational frameworks. DESIGN/METHODOLOGY/APPROACH: This multimethod study began with narrative review of existing literature on leadership for organizational frameworks in the context of patient-care quality and safety. The review informed development of scripted questions followed by semi-structured interviews with a purposeful sample of 11 healthcare leaders across Canada and the USA. FINDINGS: Key findings include consistent themes, along with indication by cited authors that important yet widely unfamiliar historical lessons from leading innovators along the past 100 years seem lost to institutional memory. Three themes emerged from the literature review: organizational ideology, the right leadership and organizational resilience. Several unique practical methods were discovered to be associated with consistent success. Additional interview themes include: a clear organizational mission, values and vision; personal values driving passion; all leaders and teams should collaborate; importance of a role model figure; importance of transparency; and flexibility to lead differently. ORIGINALITY/VALUE: Important achievements and innovations exist in isolated examples. Now is the time to rethink leadership for organizations to widely steady a continuous evolution of patient care quality and safety progress. This manuscript identifies methods for improvement that have not been considered from business literature and recognizes current perspectives of healthcare leaders, for application in the realm of patient safety.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".