Sociocultural Factors Influencing Incident Reporting Among Physicians and Nurses: Understanding Frames Underlying Self- and Peer-Reporting Practices
Bibliographic record
Abstract
OBJECTIVES: Voluntary reporting of incidents is a common approach for improving patient safety. Reporting behaviors may vary because of different frames within and across professions, where frames are templates that individuals hold and that guide interpretation of events. Our objectives were to investigate frames of physicians and nurses who report into a voluntary incident reporting system as well as to understand enablers and inhibitors of self-reporting and peer reporting. METHODS: This is a qualitative case study-confidential in-depth interviews with physicians and nurses in General Internal Medicine in a Canadian tertiary care hospital. RESULTS: Frames that health care practitioners use in their reporting practices serve as enablers and inhibitors for self-reporting and peer reporting. Frames that inhibit reporting are shared by physicians and nurses, such as the fear of blame frame regarding self-reporting and the tattletale frame regarding peer reporting. These frames are underpinned by a focus on the individual, despite the organizational message of reporting for learning. A learning frame is an enabler to incident reporting. Viewing the objective of voluntary incident reporting as learning allows practitioners to depersonalize incident reporting. The focus becomes preventing recurrence and not the individual reporting or reported on. CONCLUSIONS: Physicians and nurses use various frames that bound their views of self and peer incident reporting-further progress should incorporate an understanding of these deep-seated views and beliefs.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".