Why patients want to take or refuse to take antibiotics: an inventory of motives
Bibliographic record
Abstract
BACKGROUND: Inappropriate use of antibiotics is a worldwide issue. In order to help public health institutions and each particular physician to change patterns of consumption among patients, it is important to understand better the reasons why people accept to take or refuse to take the antibiotic drugs. This study explored the motives people give for taking or refusing to take antibiotics. METHODS: Four hundred eighteen adults filled out a 60-item questionnaire that consisted of assertions referring to reasons for which the person had taken antibiotics in the past and a 70-item questionnaire that listed reasons for which the person had sometimes refused to take antibiotics. RESULTS: A six-factor structure of motives to take antibiotics was found: Appropriate Prescription, Protective Device, Enjoyment (antibiotics as a quick fix allowing someone to go out), Others' Pressure, Work Imperative, and Personal Autonomy. A four-factor structure of motives not to take antibiotics was found: Secondary Gain (through prolonged illness), Bacterial Resistance, Self-defense (the body is able to defend itself) and Lack of trust. Scores on these factors were related to participants' demographics and previous experience with antibiotics. CONCLUSION: Although people are generally willing to follow their physician's prescription of antibiotics, a notable proportion of them report adopting behaviors that are beneficial to micro-organisms and, as a result, potentially detrimental to humans.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".