Individual-level characteristics and geospatial factors associated with cervical cancer screening participation in Alberta, Canada: a population-based cross-sectional study
Bibliographic record
Abstract
Abstract Background Cervical cancer is the fourth most common cancer in women worldwide. Effective primary prevention with human papillomavirus vaccination and secondary prevention with screening can prevent most cervical cancer cases. Cervical cancer screening uptake varies among women in underserved populations. Research that adds to the understanding of the individual and geographic area-level characteristics of women and their screening status is valuable for public health intervention planning. This study aimed to identify these characteristics related to cervical cancer screening status. Methods The study population included women between the ages of 28 to 69 years in Alberta. Data was extracted from administrative health data sources and linked to the Alberta Cervical Cancer Screening Program database to determine screening status. Descriptive bivariate analysis was conducted to describe variations in cervical cancer screening statuses and individual-level sociodemographic, health system factors, and geographic characteristics. Multinomial logistic regression analysis was conducted to investigate the relationship between these characteristics and screening participation. Geospatial analyses including heat maps were used to visualize variation in screening participation across the province. Getis-Ord Gi* hot-spot analysis was used to determine the location and magnitude of spatial autocorrelation. Results The study included 933,965 eligible women. Compared with those who are currently up-to-date for screening, those who have no record of screening tend to be older (OR: 3.63; 95% CI: 3.57 to 3.70), reside in the South Zone (OR: 1.51; 95% CI: 1.47 to 1.55), were health system non-users (OR: 2.95: 95% CI: 2.86 to 3.04), did not see a general practitioner (OR: 13.86; 95% CI: 13.32 to 14.43), or had no usual provider of care (OR: 3.227; 95% CI: 3.141 to 3.315). There are statistically significant hot spots of women who are overdue or have no record of cervical cancer screening in the North, Central, and Calgary Zones. Conclusions This study found that cervical cancer screening participation varied across geographical, health system and sociodemographic characteristics and identified clusters of regions with higher proportions of women who are under-screened in Alberta, Canada. Overall, these findings will help inform the design of interventions that aims to improve cervical cancer screening participation among underserved groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".