Bibliographic record
Abstract
© 2006 Society of Rural Physicians of Canada Can J Rural Med 2006; 11 (4) R aymond Pong and his colleagues reported this year that in 2004, rural Canada accounted for 21.1% of the population and 9.4% of all doctors. Yet the 2005 Canadian Institute for Health Information report indicates that it is easier to find a family doctor in rural communities than in the cities. What does this mean? Are rural docs superdocs? There is no doubt that as rural doctors, we play an important role in our communities. We are often leaders and stalwart advocates. We have a strong sense of responsibility and we do the best we can to look after the needs of our communities. Rural Canada has 2.4% of the specialists and 16% of family doctors. As rural physicians we have a broad scope of practice and are true generalists. Many rural family docs are competent in endoscopy, anesthesia, surgery, orthopedics, obstetrics, psychotherapy, ultrasonography and cardiac stress testing. We have developed these skill sets because they are needed by the patients in our isolated communities. Rural specialists also have broad skill sets. General surgeons in rural practice often perform cesarean sections, do burr holes and reduce fractures. We are, however, sensing an increasing reluctance from our urban counterparts and professional organizations to offer generalist and advanced skills training. The Society of Rural Physicians of Canada is forging links and collaborating with other national health organizations, especially the College of Family Physicians of Canada, and Canada’s medical schools. Such links are vital in ensuring that the rural health care component of their social mandate is fulfilled by increasing the availability of generalist and advanced skills training. On another note, the Romanow Report of 2002 identified “Rural Health Access” as one of the 5 immediate issues for targeted funding. It is the only one that has not been addressed
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.306 | 0.142 |
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".