Assessing Gaps in Comprehensive HIV Care Across Settings of Care for Women Living with HIV in Canada
Bibliographic record
Abstract
Background: Women living with HIV in Canada experience barriers to comprehensive HIV care. We sought to describe care gaps across a typology of care. Methods: We analyzed baseline data from the Canadian HIV Women's Sexual and Reproductive Health Cohort Study (CHIWOS). A typology of care was characterized by primary HIV physician and care setting. Quality-of-care indicators included the following: Pap test, Pap test discussions, reproductive goal discussions, breast cancer screening, antiretroviral therapy (ART) use, adherence, HIV viral load, and viral load discussions. We defined comprehensive care with three indicators: Pap test, viral load, and either reproductive goal discussions over last 3 years or breast cancer screening, as indicated. Multivariable logistic regression analyses measured associations between care types and quality-of-care indicators. Results: Among women living with HIV accessing HIV care, 56.4% (657/1,164) experienced at least one gap in comprehensive care, most commonly reproductive goal discussions. Women accessed care from three types of care: (1) physicians (specialist and family physicians) in HIV clinics (71.6%); (2) specialists in non-HIV clinics (17.6%); and (3) family physicians in non-HIV clinics (10.8%), with 55.5%, 63.9%, and 50.8% gaps in comprehensive care, respectively. Type 3 care had double the odds of not being on ART: adjusted odds ratio (AOR 2.09, 95% confidence interval [CI] 1.16–3.75), while Type 2 care had higher odds of not having discussed the importance of Pap tests (AOR 1.48, 95% CI 1.00–2.21). Discussion: Women continue to experience gaps in care, across types of care, indicating the need to evaluate and strengthen women-centered models of care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".