Factors affecting antibiotic decisions for upper respiratory tract infections I: A survey of family physicians
Bibliographic record
Abstract
Identifying factors associated with the ubiquitous inappropriate prescribing of antibiotics for upper respiratory tract infections (URTIs) will help develop effective interventions and decrease antimicrobial resistance. Surveys were mailed to family physicians in Ontario, Canada. The survey assessed antibiotic prescribing for URTIs and a wide range of influences on antibiotic decisions. Multivariate models of inappropriate prescribing were generated. 316 of 544 (58%) family physicians completed surveys. Associated with self-reported antibiotic prescribing for acute bronchitis were patients with obligations (OR 2.1; 95% CI, 1.2-3.6), physicians with positive antibiotic use attitudes (OR 2.1; 95% CI, 1.1-3.9), satisfaction antibiotics best for patients (OR 1.5; 95% CI, 1.1-2.1), and knowledgeable patients (OR 0.5; 95% CI, 03-0.8). Associated with antibiotic prescribing for influenza were patients with obligations (OR 2.2; 95% CI, 1.2-3.8), patients thought to be seeking antibiotics (OR 1.4; 95% CI, 1.1-1.9), and attending university and profession sponsored courses (OR 0.7; 95% CI, 0.4-1.0). Associated with not prescribing first line antibiotics for acute sinusitis were pharmaceutical industry influence (OR 2.0; 95% CI, 1.1-3.3), solo practice (OR 2.0; 95% CI, 1.1-5.0), and recommending rest and simple analgesics (OR 0.5; 95% CI, 0.3-0.8). Associated with not prescribing first line antibiotics for streptococcal pharyngitis were pharmaceutical industry influence (OR 1.7; 95% CI, 1.3-2.5), physician age (OR 1.6; 95% CI, 1.3-2.1), and perceived importance of clinical guidelines (OR 0.6; 95% CI, 0.4-0.8). Health care workers should be informed of the influence of perceived patient motivation and the pharmaceutical industry on antibiotic use for URTIs and these insights included in interventions targeting 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".