P-13 The Utility of Occupational Health Data in the Canadian Partnership for Tomorrow’s Health (CanPath)
Bibliographic record
Abstract
Introduction The Canadian Partnership for Tomorrow’s Health (CanPath) is a multi-centered prospective cohort study, and represents Canada’s largest population health research platform. CanPath holds data and biosamples on more than 330,000 participants from five regional cohorts representing British Columbia, Alberta, Ontario, Quebec, Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland and Labrador. A sixth cohort representing Manitoba has begun recruitment and Saskatchewan is in the planning stages. Objectives To examine the genetic, environmental and lifestyle factors that may influence the development of cancer and chronic disease. Methods A standardized baseline questionnaire was implemented across CanPath between 2009–2015. Participants also provided biosamples including blood, saliva, urine, and toenails, and non-invasive physical measures (height, weight, hip and waist circumference, body composition, and blood pressure). Subsequently, the first follow-up questionnaire was implemented between 2016–2018. Data from supplementary questionnaires are also available from regional cohorts. Results CanPath holds a harmonized dataset with 1,477 variables including demographics, history of cancer and other chronic disease, lifestyle and health behaviours, and physical measures. Variables of particular relevance to occupational health research include geographic location, sleep, job title, occupational history, work status, and work schedule. In addition, >150,000 participants provided blood and/or other biosamples. Conclusions CanPath represents a powerful tool for population health research. The survey data and biosamples are available to researchers for future use to gain a more in-depth understanding of the causes and consequences related to occupational health among Canadian residents.
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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 | 0.020 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".