Bibliographic record
Abstract
Dr Cathy Felderhof has practised family medicine in New Glasgow, a rural area of Nova Scotia northeast of Halifax, for more than 20 years, following in the footsteps of her father, who started the practice more than a half century ago. But the practice is no longer hers. It became the province’s fi rst patient-owned cooperative—the North Nova Health Care Co-Operative—on November 1, 2004. “Something had to give,” she told colleagues and the local media last summer. She was putting in 80hour weeks seeing to a patient list of almost 4000, doing on-call rounds at the local hospital, providing 1 day a week of services to the aboriginal community in Pictou, and “spending 40% of my time on paperwork.” A change had become “a matter of survival,” according to Dianne Kelderman, the head of the Nova Scotia Co-operative Council, who has been fi elding most questions relating to North Nova as Dr Felderhof tries to ease herself out of the public eye after a summer of media attention and passionate, sometimes rancorous, debate. Dr Felderhof ’s change of thinking about the structure of her practice started after discussions with Dr Ray Rupert, the founding president of Doctors Care Cooperative in Ontario, who put her in touch with the economic development arm for the cooperative sector in Nova Scotia. “Th e rest,” said Ms Kelderman, “is history.”
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".