Developing an Integrated Evidence-Based Medicine Curriculum for Family Medicine Residency at the University of Alberta
Bibliographic record
Abstract
There is general consensus in the academic community that evidence-based medicine (EBM) teaching is essential. Unfortunately, many postgraduate programs have significant weakness in their EBM programs. The Family Medicine Residency committee at the University of Alberta felt their EBM curriculum would benefit from critical review and revision. An EBM Curriculum Committee was created to evaluate previous components and develop new strategies as needed. Input from stakeholders including faculty and residents was sought, and evidence regarding the teaching and practical application of EBM was gathered. The committee drafted goals and objectives, the primary of which were to assist residents to (1) become competent self-directed, lifelong learners with skills to effectively and efficiently keep up to date, and 2) develop EBM skills to solve problems encountered in daily practice. New curriculum components, each evidence based, were introduced in 2005 and include a family medicine EBM workshop to establish basic EBM knowledge; a Web-based Family Medicine Desktop promoting easier access to evidence-based Internet resources; a brief evidence-based assessment of the research project enhancing integration of EBM into daily practice; and a journal club to support peer learning and growth of rapid appraisal skills. Issues including time use, costs, and change management are discussed. Ongoing evaluation of the curriculum and its components is a principal factor of the design, allowing critical review and adaptation of the curriculum. The first two years of the curriculum have yielded positive feedback from faculty and statistically significant improvement in multiple areas of residents' opinions of the curriculum and comfort with evidence-based practice.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".