Motivators and inhibitors of nurses' speaking up behaviours: A descriptive qualitative study
Bibliographic record
Abstract
AIMS: To identify factors that motivate or inhibit nurses' speaking up for patient safety. DESIGN: A descriptive qualitative study. METHODS: We conducted semi-structured interviews with 15 nurses from four Korean hospitals between December 2020 and January 2021. Data were analysed using inductive content analysis. RESULTS: We identified safety culture, supportive unit managers and role models, positive reactions from or familiarity with others, high-risk situations and personal characteristics and beliefs as motivators of nurses' speaking up. Hierarchies and power differentials, seniority and unit tenure, concerns about relationships, and heavy workloads inhibited nurses' speaking up. CONCLUSION: Individual, organizational and cultural characteristics influence nurses' decisions on whether or not to voice their concerns, suggestions or ideas. Certain characteristics of Korean culture, such as strong hierarchies and the valuing of good relationships, play an important role in nurses' speaking up behaviours. Our findings can be used to inform educational interventions and management expectations about interpersonal behaviours, especially in a culture where age- and seniority-based hierarchies and collectivism are prevalent. IMPACT: Nurses perceived speaking up as a challenging behaviour, and they sometimes withhold their voices even when speaking up is needed for patient safety. We found that individual, organizational, and contextual factors affect the speaking up behaviours of nurses. Nurse managers can create environments that are more supportive of nurses' speaking up behaviours by using inclusive leadership to create psychological safety, by inviting and showing appreciation for staff input, and by helping physicians and senior nurses understand the importance of all nurses' voices. NO PATIENT OR PUBLIC CONTRIBUTION: Patient or public contribution does not apply to this study as its purpose was to explore the speaking up experiences of nurses themselves.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".