Keeping up with the literature: A current practice survey and qualitative needs assessment among emergency physicians
Bibliographic record
Abstract
Purpose: Physicians face the challenge of staying current with a rapidly growing body of evidence and applying it to their practice. How emergency physicians (EPs) do so is unknown. The authors sought to describe and assess needs around EP patterns of evidence-based medicine (EBM) and continuing medical education (CME) resource use. Methods: The authors conducted a multicenter, cross-sectional study in 2019 across 12 tertiary care, community, and suburban emergency department (ED) sites in the greater area of Edmonton. Information on EBM/CME resource use along with barriers and facilitators to staying current was gathered using a rigorously developed survey tool, distributed electronically and by mail. Responses were tabulated and subgroups analyzed using MANOVA and ANOVA tests. Thematic analysis of comments used a phenomenological lens. Results: A total of 118 EPs (40.1%) completed the survey. Listening to podcasts, attending EM conferences, and subscription-based resources were preferred for staying current. Resource use differed by years in practice but not by age, sex, training background, or site type. EBM had an important impact on respondents' practice (average rating 3.8 out of 5, with 5 indicating "practice changing"). Time was an important barrier. Most (62.7%) felt that they did not spend enough time, despite spending a median of 4 to 5 h monthly on EBM. Facilitators (including journal club summaries or lists of practice-relevant papers) had only moderate impacts. Thematic analysis found three themes (importance of EBM, implementation challenges, and dissemination of EBM) and 13 subthemes. Conclusion: EPs preferentially chose podcasts, conferences, and subscription-based resources to stay current with EBM; time was the biggest barrier. These findings help ED leads and educators tailor CME to physician learning preferences to maximize application of EBM to clinical practice. The next steps include developing/curating resources and disseminating the survey on a larger scale to identify opportunities for shared virtual resources.
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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 | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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 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".