Teaching family medicine residents how to answer clinical questions using QUIPs
Bibliographic record
Abstract
Background: “Questions in Practice” (QUIP) rounds are used to encourage residents to quickly find, evaluate, and incorporate information into clinical practice. It is an opportunity for residents to identify a clinical question, research the answer, present the evidence, and discuss how to apply it to practice. The value of using this method to teach residents has not been evaluated. Methods: A sampling of all first and second-year family medicine residents enrolled in the Memorial University Family Medicine program were invited to participate in the survey. The survey gathered information about the residents’ current experiences with answering clinical questions, their experience during QUIP rounds, and the value of an interdisciplinary approach. Results: The response rate was 91% (42/46). Medical websites (45%) and journal article indexes (34%) were most often used. Through QUIPs, 50% of the students identified new methods to retrieve answers, 80% considered it a useful learning experience, 75% had improved confidence, and clinical knowledge improved in 97%. Conclusions: Residents are familiar with many general sources of medical information, and QUIPs helped improve confidence in their knowledge and ability to answer questions. QUIPs appear to be a useful tool for teaching information resources and how to interpret and apply evidence to clinical situations.
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 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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".