‘Sorry, I meant the patient's left side’: impact of distraction on left–right discrimination
Bibliographic record
Abstract
CONTEXT: Medical students can have difficulty in distinguishing left from right. Many infamous medical errors have occurred when a procedure has been performed on the wrong side, such as in the removal of the wrong kidney. Clinicians encounter many distractions during their work. There is limited information on how these affect performance. OBJECTIVES: Using a neuropsychological paradigm, we aim to elucidate the impacts of different types of distraction on left-right (LR) discrimination ability. METHODS: Medical students were recruited to a study with four arms: (i) control arm (no distraction); (ii) auditory distraction arm (continuous ambient ward noise); (iii) cognitive distraction arm (interruptions with clinical cognitive tasks), and (iv) auditory and cognitive distraction arm. Participants' LR discrimination ability was measured using the validated Bergen Left-Right Discrimination Test (BLRDT). Multivariate analysis of variance was used to analyse the impacts of the different forms of distraction on participants' performance on the BLRDT. Additional analyses looked at effects of demographics on performance and correlated participants' self-perceived LR discrimination ability and their actual performance. RESULTS: A total of 234 students were recruited. Cognitive distraction had a greater negative impact on BLRDT performance than auditory distraction. Combined auditory and cognitive distraction had a negative impact on performance, but only in the most difficult LR task was this negative impact found to be significantly greater than that of cognitive distraction alone. There was a significant medium-sized correlation between perceived LR discrimination ability and actual overall BLRDT performance. CONCLUSIONS: Distraction has a significant impact on performance and multifaceted approaches are required to reduce LR errors. Educationally, greater emphasis on the linking of theory and clinical application is required to support patient safety and human factor training in medical school curricula. Distraction has the potential to impair an individual's ability to make accurate LR decisions and students should be trained from undergraduate level to be mindful of this.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".