Physicians' Evidence-Based Clinical Decision-Making Practices for New Drug Prescriptions: A Qualitative Study
Bibliographic record
Abstract
Failure to use research evidence to inform decision-making is one of the reasons for the inappropriate prescribing behavior of physicians. The purpose of this study is to understand the process of physicians’ evidence-based clinical decision-making for new drug prescriptions. Eleven semi-structured interviews were conducted and thematic coding was used for data analysis. The findings suggest that at the point-of-care (POC), given the time constraints, physicians seek information from readily accessible and reliable sources such as medical websites. They use pre-appraised information sources as well as their professional networks to access critically appraised information. Expert knowledge sources (e.g., specialists, colleagues) play a crucial role throughout the process, from information seeking to application in clinical decision-making. Medical information systems facilitating immediate access to summarized reliable evidence with features to connect to the communities of practice in real-time can be an effective strategy to improve 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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".