Bibliographic record
Abstract
PROBLEM ADDRESSED: To improve integration of cancer care, Cancer Care Ontario-a provincial agency responsible for planning, advising on, implementing, and monitoring initiatives to improve cancer outcomes-proposed a primary care and cancer engagement strategy in its Ontario Cancer Plan 2008-2011. OBJECTIVE OF PROGRAM: The strategy was designed to focus initially on improving screening for colorectal cancer in primary care settings and would expand to improving primary care integration, early detection, decreased mortality, and better patient experiences throughout the whole cancer journey. PROGRAM DESCRIPTION: Following a symposium on integrating family practice and cancer care, leaders from Cancer Care Ontario and the Ontario College of Family Physicians developed an action plan. A Provincial Primary Care Lead and 13 Regional Primary Care Leads (RPCLs) were identified. Broad provincial, national, and international consultations and environmental scanning resulted in the development of a strategic conceptual framework guiding the integration initiatives of the primary care and cancer strategy. It includes 3 key domains of interest (vertical, clinical, and functional integration) surrounded by 2 broad and encompassing activities (knowledge transfer and exchange; measurement and monitoring). The RPCLs are the local contacts for primary care providers and regional cancer programs in Ontario. CONCLUSION: It is early days, but the RPCLs are already busy participating in key organizational governance structures as decision makers; acting as key contacts for primary care providers who need information about the cancer system; and helping to organize educational events. Together they are developing a strategic plan with long- and short-term goals and are advocating for the resources required to improve integration and engagement of the primary care and cancer system.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".