Rates and Correlates of Self-Reported Pap Testing in African-Caribbean-Black HIV-Positive Women in Ontario, Canada
Bibliographic record
Abstract
Cervical dysplasia and cancer are more common and aggressive in HIV-positive women and Papanicolaou (Pap) testing allows for their earlier identification and treatment.Guidelines recommend annual screening for this population.This cross-sectional study assessed prevalence and correlates of self-reported Pap testing among African-Caribbean-Black HIV-positive women who completed an ACASI-administered questionnaire.Participants were recruited through a community health centre in Toronto, Canada.Pap testing history was assessed by a single question asking when the last test was done.Logistic regression examined correlates of Pap testing in the previous year.The 126 participating women's median age was 40 years (IQR=34-46); 53.2% were East African and 16.7% Caribbean.69.9% and 82.1% of women had received a Pap test in the previous year and three years, respectively; 10.6% had never been tested.Age: 35-49 vs. >50 years (OR=6.7,95%,CI=1.7-25.1),being in Canada for >2 years (OR=4.6,95%,CI=1.6-13.5),having >2 sexual partners (OR=3.4,95%,CI=1.1-10.7)or having seen a family doctor within 6 months (OR=2.6,95%,CI=1.1-6.2) were significantly associated with Pap testing in the past year.In conclusion, 70% of participating women did have Pap screening in the past year.Program development to reach the 30% under-screened women should be sought; especially for the 10% who never received screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".