Online Searching as a Practice for Evidence-Based Medicine in the Neonatal Intensive Care Unit, University of Malaya Medical Center, Malaysia: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The use of the internet for research is essential in the practice of evidence-based medicine. The online search habits of medical practitioners in clinical settings, particularly from direct observation, have received little attention. OBJECTIVE: The goal of the research is to explore online searching for information as an evidence-based practice among medical practitioners. METHODS: A cross-sectional study was conducted to evaluate the clinical teams' use of evidence-based practice when making clinical decisions for their patients' care. Data were collected through online searches from 2015 to 2018. Participants were medical practitioners and medical students in a Malaysian public teaching hospital's neonatal intensive care unit who performed online searches to find answers to clinical questions that arose during ward rounds. RESULTS: In search sessions conducted by the participants, 311 queries were observed from 2015 to 2018. Most participants (34/47, 72%) were house officers and medical students. Most of the searches were conducted by house officers (51/99, 52%) and medical students (32/99, 32%). Most searches (70/99, 71%) were directed rather than self-initiated, and 90% (89/99) were completed individually rather than collaboratively. Participants entered an average of 4 terms in each query; three-quarters of the queries yielded relevant evidence, with two-thirds yielding more than one relevant source of evidence. CONCLUSIONS: Our findings suggest that junior doctors and medical students need more training in evidence-based medicine skills such as clinical question formulation and online search techniques for performing independent online searches effectively. However, because the findings were based on intermittent opportunistic observations in a specific clinical setting, they may not be generalizable.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".