Physicians’ information-seeking, appraising, and clinical decision-making practices for drug prescriptions: an exploratory study
Bibliographic record
Abstract
Objective: The purpose of this study is to understand the process of physicians' evidence-based clinical decision-making for new drug prescriptions. Methods: Eleven semi-structured interviews were conducted, and thematic coding was used for data analysis. Results: Several findings emerged. First, point-of-care information seeking focuses more on accessible and easy-to-use sources, such as medical websites, while out-of-practice searches encompass broader sources such as printed sources and extended networks. Medical websites are becoming preferred sources of information. Second, critical appraisal of information is performed passively by using pre-appraised information sources and referring to professional networks. Third, professional networks (i.e., specialists and senior colleagues) remain essential throughout the process and are pivotal for the decision to change prescription practices. Conclusions: Medical information systems that facilitate immediate access to summarized reliable evidence and feature real-time connectivity to the communities of practice can be an effective strategy for improving physicians' evidence-based practice for new drug prescriptions.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".