Antibiotic prescription in primary care from the perspective of family physicians: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Antibiotic consumption increases worldwide steadily. Turkey is now top on the list of global consumption and became a prototype of excessive use of antibiotics. In the last two decades, family physicians (FPs) have become key figures in the healthcare system. This study aims to understand the reasons for inappropriate antibiotic prescribing and elicit suggestions for improving antibiotic use in primary care from doctors themselves. METHODOLOGY: This is a qualitative semi-structured interview study with research dialogues guided by the Vancouver School of interpretive phenomenology. Fourteen FPs from different parts of Turkey were questioned on inappropriate antibiotic prescriptions and their suggestions for improving antibiotic use. RESULTS: The most important reasons for prescribing antibiotics without acceptable indications were patient expectations, defensive medical decision making, constraints due to workload, and limited access to laboratories. The most remarkable inference was the personal feeling of an insecure job environment of the FPs. The most potent suggestions for improving the quality of antibiotic prescription were public campaigns, improvements in the diagnostic infrastructures of primary care centers, and enhancing the social status of FPs. The FPs expressed strong concerns related to the complaints that patients make to administrative bodies. CONCLUSIONS: Primary care physicians work under immense pressure, stemming mainly from workload, patient expectations, and obstacles related to diagnostic processes. Improving the social status of physicians, increasing public awareness, and the facilitation of diagnostic procedures was the methods suggested for increasing antibiotic prescription accuracy.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".