Bibliographic record
Abstract
This brief note describes one possible solution to the problem of doctor shortages in urban Canada. It involves a family physician (B.M.) nearing the end of his career and wishing to reduce his overall workload in his fi nal years of practice. Th is family physician had hoped to pass on the care of his substantial practice in a medium-sized city of southern Ontario to a younger colleague. Unfortunately, dwindling numbers of family physicians in the 1990s were willing to take on an active practice, and B.M. was able to continue working beyond the age of 60 only with the help of a physician associate, who carried the practice for 2 days weekly on a locum tenens basis. In time, even the supply of part-time physician associates dried up. By 2002, B.M. was faced with three alternatives: return to full-time activity and simply keep working, close the practice and leave almost 3000 patients with no ongoing care, or look for creative alternatives. Having worked with nurse practitioners in the past, B.M. began to explore having one as a colleague (designated RN[EC] under current Ontario legislation) join the practice as an independent associate to take on many of the responsibilities of locum physicians employed previously. The key was to have the associate practise independently but in conformity with collaborative guidelines outlined in provincial legislation. Such arrangements have worked well in isolated areas where physician supply has been minimal, but B.M. was unaware of similar collaborations in southern Ontario.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".