Health Equity in National Cancer Control Plans: An Analysis of the Ontario Cancer Plan
Bibliographic record
Abstract
BACKGROUND: National cancer control plans (NCCPs) are important documents that guide strategic priorities in cancer care and plan for the appropriate allocation of resources based on the social, geographic and economic needs of a population. Despite the emphasis on health equity by the World Health Organization (WHO), few NCCPs have a focus on health equity. The Ontario Cancer Plan (OCP) IV, (2015 to 2019) is an example of an NCCP with clearly defined health equity goals and objectives. METHODS: This paper presents a directed-content analysis of the OCP IV health equity goals and objectives, in light of the synergies of oppression analytical framework. RESULTS: The OCP IV confines equity to an issue of access-to-care. As a result, it calls for training, funding, and social support services to increase accessibility for high-risk population groups. However, equity has a broader definition. And as such, it also implies that systematic differences in health outcomes between social groups should be minimal. This is particularly significant given that socially disadvantaged cancer patients in Ontario have distinctly poorer cancer-related health outcomes. CONCLUSION: Health systems are seeking ways to reduce the health equity gap. However, to reduce health inequities which are socially-based will require a recognition of the living and working conditions of patients which influence risk, mortality and survival. NCCPs represent a way to politically advocate for the determinants of health which profoundly influence cancer risk, outcomes and mortality.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".