Understanding nurses’ and physicians’ fear of repercussions for reporting errors: clinician characteristics, organization demographics, or leadership factors?
Bibliographic record
Abstract
Abstract Background Identifying and understanding factors influencing fear of repercussions for reporting and discussing medical errors in nurses and physicians remains an important area of inquiry. Work is needed to disentangle the role of clinician characteristics from those of the organization-level and unit-level safety environments in which these clinicians work and learn, as well as probing the differing reporting behaviours of nurses and physicians. This study examines the influence of clinician demographics (age, gender, and tenure), organization demographics (teaching status, location of care, and province) and leadership factors (organization and unit leadership support for safety) on fear of repercussions, and does so for nurses and physicians separately. Methods A cross-sectional analysis of 2319 nurse and 386 physician responders from three Canadian provinces to the Modified Stanford patient safety climate survey (MSI-06). Data were analyzed using exploratory factor analysis, multiple linear regression, and hierarchical linear regression. Results Age, gender, tenure, teaching status, and province were not significantly associated with fear of repercussions for nurses or physicians. Mental health nurses had poorer fear responses than their peers outside of these areas, as did community physicians. Strong organization and unit leadership support for safety explained the most variance in fear for both nurses and physicians. Conclusions The absence of associations between several plausible factors including age, tenure and teaching status suggests that fear is a complex construct requiring more study. Substantially differing fear responses across locations of care indicate areas where interventions may be needed. In addition, since factors affecting fear of repercussions appear to be different for nurses and physicians, tailoring patient safety initiatives to each group may, in some instances, be fruitful. Although further investigation is needed to examine these and other factors in detail, supportive safety leadership appears to be central to reducing fear of reporting errors for both nurses and physicians.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".