Testing Different Message Styles about Unnecessary Antibiotics Using an Online Platform
Bibliographic record
Abstract
Patients’ expectations are a major contributor to the unnecessary prescribing of antibiotics, yet limited research has examined how physicians can calibrate these expectations. The studies we conducted tested how varying messages could impact patients’ expectations for antibiotics and their experience of medical appointments. All the participants read a short scenario about an appointment for mild sinusitis symptoms, with the patient’s expectation of antibiotics. In Study 1, the participants (n = 1069) were randomly assigned to read a positively framed, neutral, or negatively framed message regarding unnecessary antibiotics. In Study 2, the participants (n = 1073) read a message emphasizing either the societal or personal harms of unnecessary antibiotics, or a message without additional rationale. None of our pre-registered hypotheses were supported, but our exploratory analyses indicated that the societal message increased concern about antibiotic resistance. The participants who were more concerned about resistance were less likely to ask for antibiotics, more satisfied when the physician did not prescribe them, and more likely to recommend the physician to a friend. Discussing the consequences of the different courses of action did not appear to negatively impact physician–patient rapport. These studies demonstrate an inexpensive method with which to pre-test various messages about antibiotic consumption, and suggest that such messages are not negatively received by patients.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".