Are cervical and breast cancer screening programmes equitable? The case of women with intellectual and developmental disabilities
Bibliographic record
Abstract
BACKGROUND: Effective cancer screening must be available for all eligible individuals without discrimination. Lower rates of cervical and breast cancer screening have been reported in certain groups compared with women from the general population, such as women with intellectual and developmental disabilities (IDD). Research on the factors explaining those observed differences is crucial to determine whether practices are unfair and could be improved. The aim of this population-based study was to describe cancer screening utilisation by women with IDD in Ontario, Canada compared with other women in Ontario. The specific objectives were (1) to estimate the rates of cervical and breast cancer screening among eligible women with IDD in Ontario; (2) to compare the rates of cervical and breast cancer screening between eligible women with and without IDD; and (3) to examine if any observed differences between women with and without IDD persist after factors such as age, socio-economic status, rurality and healthcare utilisation are accounted for. METHOD: This study draws women with IDD from an entire population, and draws a randomly selected comparison group from the same population. It controls for important confounders in cancer screening within the limitations of the data sources. The study was conducted using health administrative databases and registries in Ontario, Canada. Two cohorts were created: a cohort of all women identified as having an IDD and a cohort consisting of a random sample of 20% of the women without IDD. RESULTS: The proportion of women with IDD who are not screened for cervical cancer is nearly twice what it is in the women without IDD, and 1.5 times what it is for mammography. CONCLUSIONS: Findings suggest that women with IDD experience inequities in their access to cancer screening. Public health interventions targeting this population should be implemented.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".