A scoping review of undergraduate ambulatory care education
Bibliographic record
Abstract
BACKGROUND: Since a disproportionate amount of medical education still occurs in hospitals, there are concerns that medical school graduates are not fully prepared to deliver efficient and effective care in ambulatory settings to increasingly complex patients. AIMS: To understand the current extent of scholarship in this area. METHOD: A scoping review was conducted by searching electronic databases and grey literature sources for articles published between 2001 and 2011 that identified key challenges and models of practice for undergraduate teaching of ambulatory care. Relevant articles were charted and assigned key descriptors, which were mapped onto Canadian recommendations for the future of undergraduate medical education. RESULTS: Most of the relevant articles originated in the United States, Australia, or the United Kingdom. Recommendations related to faculty development, learning contexts and addressing community needs had numerous areas of scholarly activity while scholarly activity was lacking for recommendations related to inter-professional practice, the use of technology, preventive medicine, and medical leadership. CONCLUSIONS: Systems should be established to support education and research collaboration between medical schools to develop best practices and build capacity for change. This method of scoping the field can be applied using best practices and recommendations in other countries.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".