The second-hand effects of antibiotics: communicating the public health risks of drug resistance
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a threat to modern medicine, but there are challenges in communicating its urgency and scope and potential solutions to this growing problem. It is recognized that AMR has a 'language problem' and the way in which healthcare professionals communicate about AMR may not always resonate with patients. Many patients are unaware that antibiotics can have detrimental effects to those beyond the recipient, due to transmission of drug-resistant organisms. The overestimation of benefits and underestimation of risks helps to fuel demand for antibiotic use in situations where they may be of little or no benefit. To better communicate risks, clinicians may borrow the term 'second-hand' from efforts to reduce smoking cessation. We present several examples where antibiotics themselves have second-hand effects beyond the individual recipient in hospitals, long-term care homes and the community. Incorporation of the concept of the second-hand effects of antibiotics into patient counselling, mass messaging and future research may help facilitate a more balanced discussion about the benefits and risks of antibiotic use in order to use these agents more appropriately.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".