Antibiotic prescribing for respiratory tract infection: exploring drivers of cognitive effort and factors associated with inappropriate prescribing
Bibliographic record
Abstract
BACKGROUND: Antibiotics are over-prescribed for upper respiratory tract infection (URTI). It is unclear how factors known to influence prescribing decisions operate 'in the moment': dual process theories, which propose two systems of thought ('automatic' and 'analytical'), may inform this. OBJECTIVE(S): Investigate cognitive processes underlying antibiotic prescribing for URTI and the factors associated with inappropriate prescribing. METHODS: We conducted a mixed methods study. Primary care physicians in Scotland (n = 158) made prescribing decisions for patient scenarios describing sore throat or otitis media delivered online. Decision difficulty and decision time were recorded. Decisions were categorized as appropriate or inappropriate based on clinical guidelines. Regression analyses explored relationships between scenario and physician characteristics and decision difficulty, time and appropriateness. A subgroup (n = 5) verbalized their thoughts (think aloud) whilst making decisions for a subset of scenarios. Interviews were analysed inductively. RESULTS: Illness duration of 4+ days was associated with greater difficulty. Inappropriate prescribing was associated with clinical factors suggesting viral cause and with patient preference against antibiotics. In interviews, physicians made appropriate decisions quickly for easier cases, with little deliberation, reflecting automatic-type processes. For more difficult cases, physicians deliberated over information in some instances, but not in others, with inappropriate prescribing occurring in both instances. Some interpretations of illness duration and unilateral ear examination findings (for otitis media) were associated with inappropriate prescribing. CONCLUSION: Both automatic and analytical processes may lead to inappropriate prescribing. Interventions to support appropriate prescribing may benefit from targeting interpretation of illness duration and otitis media ear exam findings and facilitating appropriate use of both modes of thinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".