Contextual determinants of chronic diseases: cardiovascular disease and cancer
Bibliographic record
Abstract
Background: In Canada, cardiovascular disease (CVD) and cancer are the leading causes of mortality and morbidity in adults. Research in health geography has established the importance of contextual factors (e.g., community nutrition, and physical activity environments) as significant contributors to CVD and cancer. Objectives: The objectives of this project are to: 1) systematically review the Canadian literature on the effects of contextual exposures on chronic diseases (CVD and cancer); 2) develop a method of assessment of measuring key contextual factors; and 3) explore the variations in contextual characteristics of urban and rural areas using the pilot data collected by a Canada-wide cohort study (CVCD Alliance). Methods: Objective (1): MEDLINE, EMBASE, and CINAHL databases, and reference list of articles were searched from inception through Jan 20, 2014. English language human studies, conducted in Canada, that relate to contextual factors/built environment and chronic diseases were eligible for inclusion. Objective (2): EPOCH-1 was modified to correspond with definition of community used in CVCD. Mean agreement was calculated to measure the reliability of the modified EPOCH-1. Objective (3): Physical activity (walkscore) and nutrition (cost of food basket) environments of urban and rural areas were compared using t-test. Results: Objective (1): Review of the literature indicated that fewer fast food outlets, increased density of destinations and higher socio-economic status were associated with positive health outcomes. Objective (2): Mean agreement between raters of modified EPOCH-1 was excellent (close to 0). Objective (3): Analysis of pilot data showed that as compared to urban areas, there was a trend towards higher food costs and lower walkability in rural areas. However, this trend was not statistically significant (p>0.05). Conclusion: This project will add to the current understanding of the impact of contextual characteristics on health, and promote the development of new interventions that aim to change modifiable environmental exposures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".