A Safety Huddle Intervention in In-Patient Surgical Units: A Mixed-Methods Study
Bibliographic record
Abstract
Open communication about patient safety concerns is necessary to enable a learning environment where lessons can be learned to improve patient safety, but nurses often hesitate to speak up even in situations where their patients may be at risk. One way to create a safe environment for speaking up is through the use of unit-level daily huddles. This study aimed to assess the effects of a 12-week huddle intervention on nine unit, nurse and patient care outcomes and describe nurses’ experiences with the intervention. We used a single group, pre- and post-test mixed-methods design, with a dominant quantitative thread, and a final sample of 89 staff nurses. The intervention was conducted in four surgical units in a tertiary teaching hospital in Seoul, Korea. The intervention included two educational workshops for huddle leaders, two workshops for staff nurses, and 12-week huddles with coaching visits. We collected quantitative data on nine outcomes using online surveys before and after the intervention and qualitative data on nurse experiences of the intervention after the intervention. Paired t-tests were used for quantitative data analysis, and content analysis was used for qualitative data. We examined four unit-level outcomes (organizational learning, situation monitoring, mutual support, and speaking-up climate), three nurse-level outcomes (promotive and prohibitive voice behaviors and job satisfaction), and two patient care outcomes (patient safety and quality of care). Significant improvements were found in six of the nine outcomes. Findings from the qualitative data confirmed the benefits of the intervention but also identified challenges to huddle participation. Patient safety huddles can contribute to a learning environment by flattening hierarchies and encouraging nurses to speak up regarding safety issues. Leadership is a key in role modelling and creating the foundation for a more collaborative patient safety culture in healthcare organizations, for example, through the use of daily huddles.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".