A narrative review of ambulatory care education in Canadian internal medicine
Bibliographic record
Abstract
BACKGROUND: The Canadian healthcare system faces increasing patient volumes and complexity amidst funding constraints. Ambulatory care offers a potential solution to some of these challenges. Despite growing emphasis on the provision of ambulatory care, there has been a relative paucity of ambulatory care training curricula within Canadian internal medicine residency programs. We conducted a narrative review to understand the current state of knowledge on postgraduate ambulatory care education (ACE), in order to frame a research agenda for Canadian Internal Medicine ACE. METHODS: We searched OVID Medline, Embase, and PsycINFO for articles that included the concepts of ambulatory care and medical or health professions education from 2005-2015. After sorting for inclusion/exclusion, we analyzed 30 articles, looking for dominant claims about ACE in Internal Medicine literature. RESULTS: We found three claims. First, ACE is considered to be a necessary component of medical training because of its distinction from inpatient learning environments. Second, current models of ambulatory care clinics do not meet residency education needs. Third, ACE presents opportunities to develop non-medical expert roles. CONCLUSIONS: The findings of our narrative review highlight a need for additional research regarding ACE in Canada to inform optimal ambulatory internal medicine training structures and alignment of educational and societal needs.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".