The Relationship between Organizational Leadership for Safety and Learning from Patient Safety Events
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between organizational leadership for patient safety and five types of learning from patient safety events (PSEs). STUDY SETTING: Forty-nine general acute care hospitals in Ontario, Canada. STUDY DESIGN: A nonexperimental design using cross-sectional surveys of hospital patient safety officers (PSOs) and patient care managers (PCMs). PSOs provided data on organization-level learning from (a) minor events, (b) moderate events, (c) major near misses, (d) major event analysis, and (e) major event dissemination/communication. PCMs provided data on organizational leadership (formal and informal) for patient safety. EXTRACTION METHODS: Hospitals were the unit of analysis. Seemingly unrelated regression was used to examine the influence of formal and informal leadership for safety on the five types of learning from PSEs. The interaction between leadership and hospital size was also examined. PRINCIPAL FINDINGS: Formal organizational leadership for patient safety is an important predictor of learning from minor, moderate, and major near-miss events, and major event dissemination. This relationship is significantly stronger for small hospitals (<100 beds). CONCLUSIONS: We find support for the relationship between patient safety leadership and patient safety behaviors such as learning from safety events. Formal leadership support for safety is of particular importance in small organizations where the economic burden of safety programs is disproportionately large and formal leadership is closer to the front lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".