Do Patients’ Psychosocial Characteristics Impact Antibiotic Prescription Rates?
Bibliographic record
Abstract
Previous research suggests that the characteristics of both patients and physicians can contribute to the overuse of antibiotics. Until now, patients' psychosocial characteristics have not been widely explored as a potential contributor to the overuse of antibiotics. In this study, the relationship between a patient's psychosocial characteristics (self-reported in postal surveys in 2003) and the number of antibiotics they were prescribed (recorded in Finnish national registry data between 2004-2006) were analyzed for 19,300 working-aged Finns. Psychosocial characteristics included life satisfaction, a sense of coherence, perceived stress, hostility, and optimism. In a structural equation model, patients' adverse psychosocial characteristics were not related to increased antibiotic prescriptions in the subsequent three years. However, these characteristics were strongly associated with poor general health status, which in turn was associated with an increased number of subsequent antibiotic prescriptions. Furthermore, mediation analysis showed that individuals who used healthcare services more frequently also received more antibiotic prescriptions. The current study does not support the view that patients' adverse psychosocial characteristics are related to an increased number of antibiotic prescriptions. This could encourage physicians to actively discuss treatment options with their 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".