Cervical Cancer Screening Access for Women Who Experience Imprisonment in Ontario, Canada
Bibliographic record
Abstract
Importance: Women who experience imprisonment have higher rates of cervical cancer. Lack of access to cervical cancer screening in the community or in prison may contribute to increased cervical cancer incidence. Objectives: To determine cervical cancer screening rates for women in provincial prison in Ontario, Canada, and to compare these data with data for the general population. Design, Setting, and Participants: This retrospective cohort study used correctional and health administrative data from January 1, 2006, to December 31, 2013. Participants included Ontario women aged 21 to 69 years during the follow-up period with no history of cervical cancer or hysterectomy. Analyses were conducted between July 2017 and September 2018. Exposures: Women admitted to Ontario provincial prison in 2009 or 2010 and released in 2010 were considered exposed, and women in the general population of Ontario were considered unexposed. Main Outcomes and Measures: Whether women were overdue for cervical cancer screening at the time of admission to prison or on July 1, 2010, for the general population, defined as not having been screened in the previous 3 years, and whether women who were overdue were still overdue after 3 years. Results: There were 4553 women in the prison group and 3 647 936 women in the general population group. The median (interquartile range) age was 36 (29-43) years in the prison group and 43 (34-53) years in the general population. Women in the prison group had 2.20 times (95% CI, 2.08-2.33) the odds of being overdue for cervical cancer screening compared with women in the general population after adjusting for neighborhood income quintile, at 53.9% (95% CI, 51.8%-56.1%) compared with 32.9% (95% CI, 32.8%-33.0%) (P < .001). Women in the prison group also had nearly twice the odds of still being overdue at 3 years, with an odds ratio of 1.87 (95% CI, 1.76-1.99) after adjusting for neighborhood income quintile, and rates of still being overdue of 36.2% (95% CI, 34.5%-38.0%) compared with 21.9% (95% CI, 21.8%-21.9%) (P < .001). Conclusions and Relevance: Women who experience imprisonment have worse cervical cancer screening access than women in the general population. Work should be done to promote cervical cancer screening awareness and to improve access to acceptable screening in prison and in the community after prison release.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".