Patient safety climate (PSC) perceptions of frontline staff in acute care hospitals
Bibliographic record
Abstract
BACKGROUND: Increased awareness regarding the importance of patient safety issues has led to the proliferation of theoretical conceptualizations, frameworks, and articles that apply safety experiences from high-reliability industries to medical settings. However, empirical research on patient safety and patient safety climate in medical settings still lags far behind the theoretical literature on these topics. PURPOSE: The broader organizational literature suggests that ease of reporting, unit norms of openness, and participative leadership might be important variables for improving patient safety. The aim of this empirical study is to examine in detail how these three variables influence frontline staff perceptions of patient safety climate within health care organizations. METHODOLOGY: A cross-sectional study design was used. Data were collected using a questionnaire composed of previously validated scales. FINDINGS: The results of the study show that ease of reporting, unit norms of openness, and participative leadership are positively related to staff perceptions of patient safety climate. PRACTICE IMPLICATIONS: Health care management needs to involve frontline staff during the development and implementation stages of an error reporting system to ensure staff perceive error reporting to be easy and efficient. Senior and supervisory leaders at health care organizations must be provided with learning opportunities to improve their participative leadership skills so they can better integrate frontline staff ideas and concerns while making safety-related decisions. Finally, health care management must ensure that frontline staff are able to freely communicate safety concerns without fear of being punished or ridiculed by others.
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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 | 0.005 | 0.017 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".