Are Physicians With Better Clinical Skills on Licensing Examinations Less Likely to Prescribe Antibiotics for Viral Respiratory Infections in Ambulatory Care Settings?
Bibliographic record
Abstract
BACKGROUND: Viral respiratory infections (VRIs) are a common reason for ambulatory visits, and 35% are treated with an antibiotic. Antibiotic use for VRIs is not recommended, and it promotes antibiotic resistance. Effective patient-physician communication is critical to address this problem. Recognizing the importance of physician communication skills, licensure examinations were reformed in the United States and Canada to evaluate these skills. OBJECTIVE: To assess whether physician clinical and communication skills, as measured by the Canadian clinical skills examination (CSE), predict antibiotic prescribing for VRI in ambulatory care. RESEARCH DESIGN AND SUBJECTS: A total of 442 Quebec general practitioners and pediatricians who wrote the CSE in 1993-1996 were followed from 1993 to 2007, and their 159,456 VRI visits were identified from physician claims. MEASURES: The outcome was an antibiotic prescription from a study physician dispensed within 7 days of the VRI visit. Multivariate logistic regression analyses were used to estimate the association between antibiotic prescribing for VRI and CSE score, adjusting for physician, patient, and encounter characteristics. RESULTS: Better clinical and communication skills were associated with a reduction in the risk of antibiotic prescribing, but only for female physicians. Every 1-standard deviation increase in CSE score was associated with a 19% reduction in the risk of antibiotic prescribing (risk ratio, 0.81; 95% confidence interval, 0.68-0.97). Better clinical skills were associated with an even greater reduction in risk among female physicians with higher workloads (risk ratio, 0.48; 95% confidence interval, 0.29-0.79). CONCLUSION: Physician clinical and communication skills are important determinants of antibiotic prescribing for VRI and should be targeted by future interventions.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".