Snapshot of family medicine around the world
Bibliographic record
Abstract
OBJECTIVE: To develop an interactive, living map of family medicine training and practice; and to appreciate the role of family medicine within, and its effect on, health systems across the world. COMPOSITION OF THE COMMITTEE: A subgroup of the College of Family Physicians of Canada's Besrour Centre for Global Family Medicine developed connections with selected international colleagues with expertise in international family medicine practice and teaching, health systems, and capacity building to map family medicine globally. In 2022, this group received support from the Foundation for Advancing Family Medicine's Trailblazers initiative to advance this work. METHODS: In 2018 groups of Wilfrid Laurier University (Waterloo, Ont) students conducted broad searches of relevant articles about family medicine in different regions and countries around the world; they conducted focused interviews and then synthesized and verified information, developing a database of family medicine training and practice around the world. Outcome measures were age of family medicine training programs and duration and type of family medicine postgraduate training. REPORT: now has up-to-date country-level data on family medicine practice around the world. This publicly available information will allow such data to be correlated together with health system outputs and outcomes and will be updated as necessary through a wiki-type process. While Canada and the United States only have residency training, countries such as India have master's or fellowship programs, in part accounting for the complexity of the discipline. The maps also identify where family medicine training does not yet exist. CONCLUSION: Mapping family medicine around the world will allow researchers, policy makers, and health care workers to have an accurate picture of family medicine and its impact using relevant, up-to-date information. The group's next aim is to develop data on parameters by which performance in various domains can be measured across settings and to display these in an accessible form.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".