Residents’ clinical questions
Bibliographic record
Abstract
Objective To assess residents’ clinical questions, where they get their answers, the utility of those answers, and if an evidence-based medicine (EBM) workshop improves the use of evidence-based electronic resources. Design Prospective observational cohort study. Setting Urban family medicine teaching clinics in Edmonton, Alta, in 2007. Participants First- and second-year family medicine residents training in the family medicine teaching units. Methods An observer recorded clinical questions posed by residents in clinic, the resources used to answer these questions, and how residents thought the answers modified practice. Resources were categorized broadly as colleagues, electronic, or paper. Answer utility was ranked in decreasing order as large change, small change, confirmed, expanded knowledge, or no help. Use of resources was compared before and after an EBM workshop, and between residents under normal supervision and those in semi-independent clinics. Results Thirty-eight residents from 5 sites were observed addressing 325 questions in 114 clinical half-day sessions (420 patients). Residents had 0.8 questions per patient and answered 83.4% of questions with 1 resource (range 1 to 6). Residents made 406 attempts to answer questions, using colleagues 65.5% of the time (93.6% were preceptors), electronic resources 20.7% of the time, and paper resources 13.8% of the time. Answers from colleagues were least likely to require secondary resources (F test, P < .001). The utility of answers from colleagues (F test, P = .002) was superior to that of answers from electronic resources, and this difference remained significantly higher in sensitivity analysis. The EBM workshop training did not influence electronic resource use (17.8% before and 15.1% after, Fisher-Freeman-Halton test, P = .18), but semi-independence from preceptors increased the use of electronic resources from 16.5% to 51.0% (Fisher-Freeman-Halton test, P < .001). Conclusion Residents have many questions during clinical practice. Preceptors were used more commonly than all other resources combined and were the most dependable resource for residents to obtain answers. Although an EBM workshop was not associated with increased use of electronic evidence-based resources, semi-independent work appeared to be.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".