Promoting health by empowering women, strengthening partnerships, and enhancing health care systems: One Pap test at a time
Bibliographic record
Abstract
Invasive cervical cancer, a highly preventable disease, is the thirteenth most common form of cancer among Canadian women and third amongst those women 20 to 40 years of age (Public Health Agency of Canada [PHAC], 2009). Health care providers (HCP)s know that adherence to the Canadian recommendations for regular screening, using the Pap test, reduces incidence and mortality rates (Marcus & Crane, 1998). Yet, only 30% of women in Newfoundland and Labrador consistently participate in cervical screening (Newfoundland and Labrador CHI, 2006) and mortality rates are alarming. The most recent data reveal that the incidence in 1998 was 1.5 times the national average (Health Canada, 1998) while mortality was estimated at 2.5 times the national average (NLCHI, 2006). A two-phased study conducted in Newfoundland and Labrador sought an in-depth understanding of women's perceptions, beliefs and attitudes associated with cervical cancer screening, reasons for non-participation, and personal insights to improve the screening experience. Seven main themes are identified: physical factors, emotional factors, life gets in the way, lack of education, health care providers, cultural impact, and birth control/pregnancy. Implications for nursing practice and future research are discussed.
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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 | 0.004 | 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 | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".