Adherence Over Time to Cervical Cancer Screening Guidelines: Insights From the Canadian National Population Health Survey
Bibliographic record
Abstract
BACKGROUND: A substantial percentage of North American women are nonadherent to cervical cancer screening guidelines despite the effectiveness of the Papinicolaou (pap) test for papillomavirus. Our objective was to determine factors associated with changes in adherence for cervical cancer screening guidelines over a 14-year period. METHODS: Using data from cycles 1 (1994-1995) through 7 (2006-2007) of the Canadian National Population Health Survey, we used logistic regression to compare the regularity of pap testing (at least once every 36 months) among women. We compared women with increasing adherence to pap testing guidelines to those who were never adherent, and women with decreasing adherence to those who were always adherent. The sample included women aged 20-70 years who responded in at least three of seven waves of data collection and had not undergone a hysterectomy (n=4949). Independent variables were based on Andersen's Behavioral Model of predisposing, enabling, and need variables. RESULTS: The majority of our sample were either always adherent (61.4%) or had increasing adherence (9.9%) over the course of the study. Another 4.8% were never adherent, and 6.6% had decreasing adherence over their involvement in the study. Predominantly, both enabling (e.g., presence of regular doctor) and need (e.g., birth control pill use, obesity) factors were associated with changing patterns of adherence. CONCLUSIONS: Physicians have a crucial role to play in the trajectories of adherence to cervical cancer screening guidelines over time. In addition, women with obesity need to be particularly targeted for services because they are vulnerable to negative trajectories in adherence over time.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".