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.
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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".