Brief report: cervical cancer screening in women with intellectual and developmental disabilities who have had a pregnancy
Bibliographic record
Abstract
BACKGROUND: Women with intellectual and developmental disabilities (IDD) have lower cervical cancer screening rates than women without IDD. Key barriers to screening uptake include physician or caregiver assumptions that screening is unnecessary because women with IDD are not sexually active. Our objective was to compare cervical cancer screening rates in women with and without IDD who had had a pregnancy. METHOD: We conducted a population-based retrospective cohort study using linked Ontario (Canada) health and social services administrative data. We identified 20- to 64-year-old women with (N = 5033) and without (N = 527 437) IDD who had had a pregnancy. We examined the occurrence of cervical cancer screening between April 1, 2007 and March 31, 2010. We compared screening rates in women with and without IDD using logistic regression, controlling for age, region of residence, neighbourhood income quintile and morbidity level. RESULTS: Women with IDD who had had a pregnancy were more likely than those without IDD to be young, to live in the lowest neighbourhood income quintile, to live in rural areas and to have high or very high morbidity. Even after controlling for these factors, women with IDD were less likely than women without IDD to be screened (67.7% vs. 77.0%; adjusted odds ratio 0.61; 95% confidence interval 0.58-0.65). CONCLUSIONS: Even among women who have had a pregnancy and are therefore known to have been sexually active, women with IDD face significant disparities in cervical cancer screening. Strategies to promote equitable uptake of cervical cancer screening for women with IDD need to 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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".