Addressing Inequity in Spatial Access to Lung Cancer Screening
Bibliographic record
Abstract
BACKGROUND: The successful implementation of an equitable lung cancer screening program requires consideration of factors that influence accessibility to screening services. METHODS: Using lung cancer cases in British Columbia (BC), Canada, as a proxy for a screen-eligible population, spatial access to 36 screening sites was examined using geospatial mapping and vehicle travel time from residential postal code at diagnosis to the nearest site. The impact of urbanization and Statistics Canada's Canadian Index of Multiple Deprivation were examined. RESULTS: < 0.001). Ninety-nine percent of patients with ≥60 min drive times lived in rural areas. Drive times were associated with sex, ethnocultural composition, situational vulnerability, economic dependency, and residential instability. For example, the percentage of cases with drive times ≥60 min among the least deprived situational vulnerability group was 4.7% versus 44.4% in the most deprived group. CONCLUSIONS: Populations at risk in rural and remote regions may face more challenges accessing screening services due to increased travel times. Drive times increased with increasing sociodemographic and economic deprivations highlighting groups that may require support to ensure equitable access to lung cancer screening.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".