Examining prevalence of cancer risk factors across Ontario for the Ontario Cancer Profiles tool
Bibliographic record
Abstract
The Ontario Cancer Profiles is an interactive dashboard for the public containing population-level cancerstatistics created by Ontario Health (Cancer Care Ontario). The tool contains data on cancer burden, cancerscreening measures, and cancer risk factors by Local Health Integration Network (LHIN) and Public Health Unit (PHU). It can be used for health system planning, measuring health systems performance, monitoring the impact of interventions, and to help identify new areas of research. There were 9 new modifiable cancer risk factors proposed to be included in future updates of the dashboard. The proposed risk factors include: access to care, active transportation, binge drinking, alcohol abstinence, inadequate fruit consumption, inadequate vegetable consumption, sedentary behaviour, second-hand smoke exposure, and sun safety. My practicum consisted of two main objectives: to conduct a literature review on the association between the proposed risk factors and cancer and to determine the prevalence of exposure of the identified risk factors in Ontario using 2015 to 2017 CCHS data. I performed a literature review to examine current evidence linking each proposed risk factor with cancer risk to determine the inclusion or exclusion of the indicator in the analysis. An analysis was performed with the selected variables in CCHS. Each indicator was age-standardized, and both standardized and crude ratios of individuals engaging in selected indicator activities were calculated. The results were examined for reliability using the produced coefficient of variation values. The estimates for each risk indicators allowed for the identification of target population that may be at higher risk of developing cancer due to greater exposure to the risk factors. They also serve as useful predictors for areas of improvement in regions with a high prevalence, such as healthy living within the community, and a guide to implementing preventative measures, screening, or treatment plans that may have been lacking.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".