A descriptive analysis of a novel intervention to help residents become evidence users
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To evaluate the educational and clinical effectiveness of the 'Brief Evidence-Based Assessment of Research' (BEAR), a template to assist residents in searching, evaluating and integrating relevant medical literature into daily practice. METHODS: We completed a descriptive analysis of BEARs submitted by first year residents between 2005 and 2007 at the University of Alberta Family Medicine Residency program. RESULTS: 317 BEARs were analyzed. The most common type of question for which information was searched was therapy (59%). Residents searched Pubmed most often (38%) followed by Summary (i.e. Clinical Evidence) (22%) and Filtered sites (i.e. ACP Journal Club) (19%). Original research articles were the largest resource category used to answer questions (41%). Secondary peer-reviewed resources (filtered articles, summary sites, reviews/meta-analysis and guidelines) accounted for 48% of all resources used. 19% of residents reported a large change in practice with completion of the BEAR, 50% reported a small change, 12% stated they were reassured and 8% reported that the intervention was of no help to them. CONCLUSIONS: The BEAR facilitates the use of a variety of resources in answering clinical questions. 69% of users reported at least a small change in clinical practice, suggesting that the BEAR may be a useful tool in evidence-based resident education.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 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".