Barriers and Facilitators to Communicating Nursing Errors in Long-term Care Settings
Bibliographic record
Abstract
OBJECTIVE: To explore nurses' perceptions about communicating nursing errors. DESIGN: Cross-sectional, descriptive study. PARTICIPANTS: Approximately 289 nurses working in long-term care facilities in Ontario, Canada. METHODS: A cross-sectional, descriptive study of approximately 289 nurses working in long-term care facilities in Ontario, Canada. Solicited nurses' perceptions concerning the disclosure of nursing errors and adverse events by including an open-ended item at the conclusion of a 60-item (multiple choice) questionnaire on the same topic. A qualitative content analysis was conducted using a multi-step process. RESULTS: A total of 245 responses were included in the content analysis. The main categories related to error communication that were derived from the analysis were as follows: (1) differences in the definition of terms; (2) the day-to-day working conditions and their impact on defining and reporting errors; (3) organizational factors that both help and hinder the reporting of errors in ensuring both personal and organizational responsibility; (4) communication styles that both help and hinder disclosure and adherence to proper protocols; and (5) external factors such as policies and professional standards and codes of ethics, which can provide clarity of process; and (6) recommendations for implementation of professional standards in long-term care settings to facilitate supportive working conditions. CONCLUSION: Eliminating the barriers to error communication requires moving toward a culture of safety. This involves both top-down and bottom-up approaches that allow nurses to feel comfortable being active participants in the error communication process.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".