Nature of Blame in Patient Safety Incident Reports: Mixed Methods Analysis of a National Database
Bibliographic record
Abstract
PURPOSE: A culture of blame and fear of retribution are recognized barriers to reporting patient safety incidents. The extent of blame attribution in safety incident reports, which may reflect the underlying safety culture of health care systems, is unknown. This study set out to explore the nature of blame in family practice safety incident reports. METHODS: We characterized a random sample of family practice patient safety incident reports from the England and Wales National Reporting and Learning System. Reports were analyzed according to prespecified classification systems to describe the incident type, contributory factors, outcomes, and severity of harm. We developed a taxonomy of blame attribution, and we then used descriptive statistical analyses to identify the proportions of blame types and to explore associations between incident characteristics and one type of blame. RESULTS: Health care professionals making family practice incident reports attributed blame to a person in 45% of cases (n = 975 of 2,148; 95% CI, 43%-47%). In 36% of cases, those who reported the incidents attributed fault to another person, whereas 2% of those reporting acknowledged personal responsibility. Blame was commonly associated with incidents where a complaint was anticipated. CONCLUSIONS: The high frequency of blame in these safety, incident reports may reflect a health care culture that leads to blame and retribution, rather than to identifying areas for learning and improvement, and a failure to appreciate the contribution of system factors in others' behavior. Successful improvement in patient safety through the analysis of incident reports is unlikely without achieving a blame-free culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".