Importance of safety climate, teamwork climate and demographics: understanding nurses, allied health professionals and clerical staff perceptions of patient safety
Bibliographic record
Abstract
BACKGROUND: There is growing evidence regarding the importance of contextual factors for patient/staff outcomes and the likelihood of successfully implementing safety improvement interventions such as checklists; however, certain literature gaps still remain-for example, lack of research examining the interactive effects of safety constructs on outcomes. This study has addressed some of these gaps, together with adding to our understanding of how context influences safety. PURPOSE: The impact of staff perceptions of safety climate (ie, senior and supervisory leadership support for safety) and teamwork climate on a self-reported safety outcome (ie, overall perceptions of patient safety (PS)) were examined at a hospital in Southern Ontario. METHODS: Cross-sectional survey data were collected from nurses, allied health professionals and unit clerks working on intensive care, general medicine, mental health or emergency department. RESULTS: Hierarchical regression analyses showed that perceptions of senior leadership (p<0.001) and teamwork (p<0.001) were significantly associated with overall perceptions of PS. A non-significant association was found between perceptions of supervisory leadership and the outcome variable. However, when staff perceived poorer senior leadership support for safety, the positive effect of supervisory leadership on overall perceptions of PS became significantly stronger (p<0.05). PRACTICE IMPLICATIONS: Our results suggest that leadership support at one level (ie, supervisory) can substitute for the absence of leadership support for safety at another level (ie, senior level). While healthcare organisations should recruit into leadership roles and retain individuals who prioritise safety and possess adequate relational competencies, the field would now benefit from evidence regarding how to build leadership support for PS. Also, it is important to provide on-site workshops on topics (eg, conflict management) that can strengthen working relationships across professional and unit boundaries.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".