Factors and Priorities Influencing Satisfaction with Care among Women Living with HIV in Canada: A Fuzzy Cognitive Mapping Study
Bibliographic record
Abstract
Engagement along the HIV care cascade in Canada is lower among women compared to men. We used Fuzzy Cognitive Mapping (FCM), a participatory research method, to identify factors influencing satisfaction with HIV care, their causal pathways, and relative importance from the perspective of women living with HIV. Building from a map of factors derived from a mixed-studies review of the literature, 23 women living with HIV in Canada elaborated ten categories influencing their satisfaction with HIV care. The most central and influential category was "feeling safe and supported by clinics and healthcare providers", followed by "accessible and coordinated services" and "healthcare provider expertise". Participants identified factors that captured gendered social and health considerations not previously specified in the literature. These categories included "healthcare that considers women's unique care needs and social contexts", "gynecologic and pregnancy care", and "family and partners included in care." The findings contribute to our understanding of how gender shapes care needs and priorities among women living with HIV.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".