Income, Patient Enrolment Model and Cervical Cancer screening uptake within the Central East Local Health Integration Network
Bibliographic record
Abstract
Cervical cancer screening detects cancer at early stages and is available to Ontario women ages 21 to 69 years of age. Notwithstanding cancer screening initiatives, sub-groups of Ontario women are under-screened based on current literature. The most common primary health care delivery system in Ontario are patient enrolment models (PEMs) which allows for physician-incentives when rostering and cancer screening benchmarks are met. Notwithstanding, little is known about the effect of PEM enrolment and other socioeconomic (SES) factors, such as income, on screening uptake. This study considered differences in cervical screening uptake by PEM status and neighbourhood income levels by women residing in the Central East Local Health Integration Network (CELHIN). A descriptive, comparative study using record level, administrative data from Cancer Care Ontario of eligible CELHIN women between January 1, 2012 and June 30, 2015 was conducted (N=490, 574). The variables of interest were cervical screening uptake (dependent variable), PEM status (primary exposure variable), neighbourhood income quintile (independent variable) and controlled for age and rurality. Using logistic regression, it was determined non-enrolled women were more likely not to be screened (OR =6.98, 95% CI, 6.87-7.08) compared to enrolled women, representing the strongest association. Given heterogeneous effects in odds ratios, multivariate stratified logistic regression analyses were undertaken for PEM enrolled and non-enrolled women separately. A significant association was found between older, non-enrolled women (ages 60-69) and not being screened (OR=1.87, 95% CI, 1.78-1.96). Unexpectedly, enrolled women in the lowest neighbourhood income quintile were more likely to not be screened (OR=1.49, 95% CI, 1.46-1.53) compared to their non-enrolled counterparts (OR=1.20, 95% CI, 1.14-1.25). Urban dwellers were slightly less likely to be screened (enrolled women: OR=1.10, 95% CI, 1.07-1.12; non-enrolled women, OR=1.06, 95% CI 1.01-1.11) relative to rural women. As older women have the greatest risk of high-grade invasive cervical cancer and PEM status is not protective for women living in lower SES, the priority for the CELHIN should be addressing barriers to cervical screening uptake, regardless of PEM status, for marginalized at-risk women including older women, and women living in lower SES environments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".