Information-Seeking Behaviors of Medical Students: A Cross-Sectional Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: Medical students face an information-rich environment in which retrieval and appraisal strategies are increasingly important. OBJECTIVE: To describe medical students' current pattern of health information resource use and characterize their experience of instruction on information search and appraisal. METHODS: We conducted a cross-sectional web-based survey of students registered in the four-year MD Program at Dalhousie University (Halifax, Nova Scotia, and Saint John, New Brunswick, sites), Canada. We collected self-reported data on information-seeking behavior, instruction, and evaluation of resources in the context of their medical education. Data were analyzed using descriptive statistics. RESULTS: Surveys were returned by 213 of 462 eligible students (46.1%). Most respondents (165/204, 80.9%) recalled receiving formal instruction regarding information searches, but this seldom included nontraditional tools such as Google (23/107, 11.1%), Wikipedia, or social media. In their daily practice, however, they reported heavy use of these tools, as well as EBM summaries. Accessibility, understandability, and overall usefulness were common features of highly used resources. Students identified challenges managing information and/or resource overload and source accessibility. CONCLUSIONS: Medical students receive instruction primarily on searching and assessing primary medical literature. In their daily practice, however, they rely heavily on nontraditional tools as well as EBM summaries. Attention to appropriate use and appraisal of nontraditional sources might enhance the current EBM curriculum.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".