A Behavioral Analysis of Factors That Influence Antibiotic Prescribing in Hospitals: A Metasynthesis of Reviews
Bibliographic record
Abstract
Antibiotic resistance is a global public health threat driven, in part, by antibiotic overprescription. Behavior change theories are increasingly used to try to modify prescriber behavior. A metasynthesis of 8 reviews was conducted to identify factors influencing antibiotic prescribing for adults in hospital settings and to analyze these factors using 4 behavior change frameworks. Forty-three factors were identified across 7 thematic categories and then mapped to the theoretical domains framework and capability-opportunity-motivation model of behavior. The behavior change wheel and behavior change techniques taxonomy were then used to identify appropriate interventions and their components. The domain "environmental context and resources" was coded the most often, followed by "social influences" and "beliefs about consequences," revealing that prominent sources of antibiotic prescribing behavior are "physical opportunity" and "social opportunity." Based on these results, suggested interventions include environmental prompts/cues, education on consequences of antibiotic overuse, social comparison and support, and incentives.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".