Investigating Factors Associated with not Reporting Medical Errors From the Medical Team’S Point of View in Jahrom, Iran
Bibliographic record
Abstract
BACKGROUND: medical errors as a problematic fact in healthcare systems can increase patient's safety if reported. This article tried to determine several factors associated with not reporting medical errors from medical team's points of view. METHODS: 300 staff working in different parts of educational hospitals affiliated to Jahrom University of Medical Sciences including nursing, midwifery, paramedical and medical groups participated in this descriptive study using census method (2013). Data collection was performed using a researcher-made questionnaire including 31 items regarding four areas: medical teams, managers, errors and patients. RESULTS: The mean score of factors related to errors, mangers, medical teams, and patients' scope was 2.68 ± 0.79, 2.63 ± 0.72, 2.53 ± 0.66, 2.41 ± 0.87, respectively. In medical teams' points of view, errors and managers were among the important factors for not reporting professional errors. The most important factors in professional errors were related to severity and emergency of errors (2.73 ± 0.97), and managers' focus on wrongdoers instead of noticing systematic factors of errors (3.00 ± 1.01). In medical teams, fear of legal prosecution by patients or their relatives (2.87 ± .97), and in patients, unawareness of errors (2.67 ± 1.08) was reported as the most effective factors. CONCLUSION: Factors related to errors and managers were more important than other reasons. Therefore, educating medical teams on recognizing errors and managers' proper reactions in case of occurring or reporting errors seem to be necessary.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".