Clinical Questions Addressed by First-Year Medical Students in Primary Care: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In the context of an evidence-based medicine theme, medical students in their first year at McGill University formulate a PICO (population, intervention, comparator, and outcome) question arising from a patient encounter in family medicine. We sought to analyze clinical questions addressed within PICO projects submitted by first-year medical students shadowing a family physician. METHODS: A total of 180 student projects were split equally between two reviewers. Questions were then classified according to a three-component classification system: (a) type of question (screening, diagnosis, prognosis, treatment [including preventive treatment], etiology, and harm); (b) Ely's taxonomy; and (c) question topics based on the 105 priority topics of the College of Family Physicians of Canada. RESULTS: The most frequent question type among the students was treatment/prevention (152, 84.0%), followed by etiology (7, 3.9%), screening (6, 3.3%), prognosis (6, 3.3%), harm (5, 2.8%), and diagnosis (4, 2.2%). Based on Ely's taxonomy, the most frequent question was "How should I treat condition x (not limited to drug treatment)?" (105, 58.3%). Of the 105 priority topics from the College of Family Physicians of Canada, in children (18, 10%), pain (16, 8.9%), pregnancy (12, 6.7%), depression (11, 6.1%), and behavioral problems (10, 5.6%) were most frequently represented. CONCLUSIONS: Clinical questions addressed by first-year medical students, of which the vast majority are about treatment and prevention, can be classified. Students did not commonly address questions related to diagnosis, indicating that additional teaching may be required to use the PICO format to address this question type.
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 | 0.010 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".