What do they contribute? Family medicine residents who practise in cities.
Bibliographic record
Abstract
OBJECTIVE: To determine how a cohort of family practice residents graduating between 1990 and 1997 was serving the needs of urban populations in British Columbia. DESIGN: Survey using mailed questionnaire. SETTING: British Columbia. PARTICIPANTS: All graduates of the British Columbia family practice residency program between 1990 and 1997. MAIN OUTCOME MEASURES: Graduates who were currently practising as family physicians and providing medical care to urban and inner-city populations of more than 100 000, sex, practice profiles, and a comparison with Janus Project data for British Columbia. RESULTS: Of 287 graduates surveyed, 206 responded (71.8%). Less than half (86) identified themselves as practising in urban settings; 61 of those were practising as family physicians. These physicians offered a range of primary care services; many offered inpatient and obstetric care. In addition, many were offering care to disadvantaged inner-city populations with unique and challenging medical problems. CONCLUSION: Recent graduates in family medicine practising in urban and inner-city areas are offering full-service primary care and are not abandoning it for more episodic high-volume medical 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".