Psychological Safety as a Mediator of the Relationship Between Inclusive Leadership and Nurse Voice Behaviors and Error Reporting
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine psychological safety as a mediator of the relationship between inclusive leadership and nurses' voice behaviors and error reporting. Voice behaviors were conceptualized as speaking up and withholding voice. DESIGN: This correlational study used a web-based survey to obtain data from 526 nurses from the medical/surgical units of three tertiary general hospitals located in two cities in South Korea. METHODS: We used model 4 of Hayes' PROCESS macro in SPSS to examine whether the effect of inclusive leadership on the three outcome variables was mediated by psychological safety. FINDINGS: Mediation analysis showed significant direct and indirect effects of nurse managers' inclusive leadership on each of the three outcome variables through psychological safety after controlling for participant age and unit tenure. Our results also support the conceptualization of employee voice behavior as two distinct concepts: speaking up and withholding voice. CONCLUSIONS: When leader inclusiveness helps nurses to feel psychologically safe, they are less likely to feel silenced, and more likely to speak up freely to contribute ideas and disclose errors for the purpose of improving patient safety. CLINICAL RELEVANCE: Leader inclusiveness would be especially beneficial in environments where offering suggestions, raising concerns, asking questions, reporting errors, or disagreeing with those in more senior positions is discouraged or considered culturally inappropriate.
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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 | 0.003 | 0.021 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".