Why Patients in Uruguay Agree to Take or Refuse to Take Antibiotics? An Inventory of Motives
Bibliographic record
Abstract
Objectives: We explored the motives for why patients in Uruguay take or refuse to take antibiotic drugs. Methods: We had 350 adults complete a 60-item questionnaire with statements referring to reasons for which the person had taken antibiotics in the past, and a 70-item questionnaire with reasons for which the person had sometimes refused to take antibiotics. Results: We found a 4-factor structure of motives for taking antibiotics: Appropriate Prescription , Enjoyment (antibiotics as a quick fix allowing someone to go out), Dealing with Daily Life Issues , and Avoiding Negative Consequences (mainly negative societal consequences). We found a 7-factor structure of motives to refuse to take antibiotics: Secondary Gain (through prolonged illness), Bacterial Resistance , Self-defense (the body is able to defend itself), Lack of Trust , Costs , Hostility (not contributing to pharmaceutical companies′ increases in benefits) and Dislike . Scores on these factors were related to participants′ demographics and previous experience with antibiotics. Conclusions: Uruguayan people are generally willing to follow their physician's prescription for antibiotics. Some of the motives for refusing antibiotic therapy may be grounded more on emotional reactions than on scientific arguments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".