Is cancer prevention influenced by the built environment? A multidisciplinary scoping review
Bibliographic record
Abstract
The built environment is a significant determinant of human health. Globally, the growing prevalence of preventable cancers suggests a need to understand how features of the built environment shape exposure to cancer development and distribution within a population. This scoping review examines how researchers across disparate fields understand and discuss the built environment in primary and secondary cancer prevention. It is focused exclusively on peer-reviewed sources published from research conducted in Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States from 1990 to 2017. The review captured 9958 potential results in the academic literature, and this body of results was scoped to 268 relevant peer-reviewed journal articles indexed across 13 subject databases. Spatial proximity, transportation, land use, and housing are well-understood features of the built environment that shape cancer risk. Built-environment features predominantly influence air quality, substance use, diet, physical activity, and screening adherence, with impacts on breast cancer, lung cancer, colorectal cancer, and overall cancer risk. The majority of the evidence fails to provide direct recommendations for advancing cancer prevention policy and program objectives for municipalities. The expansion of interdisciplinary work in this area would serve to create a significant population health impact.
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 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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".