Identifying At-Risk Communities for Action on Cancer Prevention: A Case Study in New Brunswick (Canada) Communities
Bibliographic record
Abstract
Health statistics reported by large geographic area such as province, state, county or health region offer little insight into disease conditions at the community level where people live and work, where occupational and environmental exposures occur, and where industrial emissions are often concentrated. This study investigated overall patterns of cancer incidence and socioeconomic status (SES) among 14 communities in the province of New Brunswick (Canada). A multivariate ordination technique, hierarchical clustering, and permutation procedures were used to identify and test significance of community clusters and whether the overall pattern of SES was correlated with patterns of cancer among communities. Communities with significantly high or significantly low overall rates of cancers were identified, patterns that were not related to SES. The potential influence of age, small populations, diagnostic screening, smoking and environmental risk factors contributing to locally elevated cancer rates are discussed. Cancer incidence reported at smaller spatial scales provides health officials and researchers with a basis for identifying communities potentially at-risk and aids in the development of appropriate community-based risk reduction actions and cancer prevention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".