Bridging the gap between primary care and the cancer system: the UPCON Network of CancerCare Manitoba.
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: Patient care is poorly coordinated between family physicians and the cancer system and the working relationships are not strong. OBJECTIVE OF PROGRAM: To improve integration of patient care and communication between FPs and cancer specialists; enhance FPs' knowledge of cancer and the cancer system; and promote the role of primary care within the cancer care system. PROGRAM DESCRIPTION: The Uniting Primary Care and Oncology (UPCON) Network of CancerCare Manitoba has created partnerships with 12 primary care clinics in Winnipeg, Man, by providing the following: access to the provincial electronic medical record for cancer; small group continuing professional development for a "lead physician" from each clinic to make him or her the local cancer resource; educational outreach to all clinic staff; and changes within CancerCare Manitoba to highlight the role of FPs. CONCLUSION: Lead physicians are appreciated by their clinic colleagues, and these FPs are the main users of the cancer electronic medical record. A strong cancer continuing professional development program has been implemented and a voice for primary care has been created within the agency. The UPCON Network is now expanding throughout Manitoba.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 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".