Predictors of inappropriate antibiotic prescribing among primary care physicians
Bibliographic record
Abstract
BACKGROUND: Inappropriate use of antibiotics promotes antibiotic resistance. Little is known about physician characteristics that may be associated with inappropriate antibiotic prescribing. Our objective was to assess whether physician knowledge, time in practice, place of training and practice volume explain the differences in antibiotic prescribing among physicians. METHODS: A historical cohort of 852 primary care physicians in Quebec who became certified between 1990 and 1993 was followed for their first 6-9 years of practice (1990-1998). We evaluated whether inappropriate antibiotic prescribing had occurred during the study period (1990-1998) for viral (prescription of antibiotics) and bacterial (prescription of second-or third-line antibiotics given orally) infections. We used logistic regression to estimate the independent contributions of time in practice, practice volume, place of medical training and scores on licensure examinations. Physician sex and visit setting were controlled for, as were patient age, sex, education, income and geographic area of residence. RESULTS: A total of 104 230 patients who received a diagnosis of a viral infection and 65 304 who received a diagnosis of a bacterial infection were included in our study. International medical graduates were more likely than University of Montréal graduates to prescribe antibiotics for viral respiratory infections (risk ratio [RR] 1.78, 95% confidence interval [CI] 1.30-2.44). Inappropriate antibiotic prescribing increased with time in practice. Physicians with a high practice volume were more likely than those with low practice volume to prescribe antibiotics for viral respiratory infections (RR 1.27, 95% CI 1.09-1.48) and to prescribe second-and third-line antibiotics as first-line treatment (RR 1.20, 95% CI 1.06-1.37). Physician scores on licensure examinations were not predictive of inappropriate antibiotic prescribing. INTERPRETATION: International medical graduates, physicians with high-volume practices and those who were in practice longer were more likely to prescribe antibiotics inappropriately. Developing effective interventions will require increased knowledge of the mechanisms that underlie these predictors of inappropriate antibiotic prescribing.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".