To report or not to report: factors that influence physician error reporting behaviour
Bibliographic record
Abstract
A significant amount of evidence affirms that medical errors increase the risk of \ninjury and death. In an ideal health care environment, physicians and health care \norganizations would accurately report all medical errors to mitigate reoccurrences and \nincrease patient safety. However, the true incidence of physician error is largely unknown, \nin part, due to underreporting. This under-reporting results in a lack of important data, \nwhich may help us understand and correct error contributors and system design failures. \nIn addition, these errors also pose a significant financial burden on hospital expenses and \nthe healthcare system as a whole. The purpose of this major paper is to identify and \ndescribe, the types of physician error and their causes, the reporting mechanisms of error, \nand the predominant factors which can affect physician error reporting behaviour. The \n"Theory of Planned Behaviour" is used as a means of identifying and illustrating how \ncertain factors can influence error reporting behaviour. Each factor associated with \nphysician error reporting is explained in relation the theory's constructs. The literature \nsearch was conducted electronically using the Laurentian University Library to access \nvarious journals and platforms. The predominant factors that influence reporting are: that \nreporting system and process, psychological safety, manager and supervisor support, \nfeedback for patient safety improvement, teamwork and peer support, as well as lack of \ntime. By addressing the barriers and enablers of physician error reporting, patient safety \nwould increase. We can ensure the same errors do not occur again by addressing the \npredominant factors that affect reporting. Despite efforts to increase reporting, further \nresearch and implementation of practices are needed to reduce the barriers that influence \nphysician error reporting and increase the enablers.
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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 | 0.015 | 0.224 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".