Engagement in and continuity of HIV care among African and Caribbean Black women living with HIV in Ontario, Canada
Bibliographic record
Abstract
Engagement in care is a key component of the HIV care cascade, yet there are knowledge gaps regarding how to assess HIV care engagement. This study aimed to develop a tool to assess HIV care engagement and to assess associations between HIV care engagement and quality of life (QOL) among African, Caribbean and Black (ACB) women living with HIV (WLWH). We conducted a cross-sectional survey with ACB WLWH across Ontario, Canada. We developed the ‘HIV Engagement in and Continuity of Care Scale’ (HECCS). We conducted exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to test the scale’s factor structure. We conducted structural equation modeling (SEM) with maximum likelihood estimation to examine the associations between the HECCS and QOL. EFA yielded four factors: access to care, care by doctor/health professionals, control of HIV care, and appointment timekeeping. The CFA of the HECCS demonstrated good model fit: χ 2 (DF: 1; n = 173) = 1.175, p = 0.278; CFI: 0.998; Tucker-Lewis Index (TLI): 0.990; RMSEA: 0.032. The HECCS was associated with increased QOL. The model fit the data well: χ 2 (DF: 31, n = 173) = 51.19, p = 0.013; CFI = 0.955; TLI = 0.934; RMSEA = 0.062. Engagement in and continuity of care is multifaceted. We recommend interventions to promote the institutional capacity to better engage ACB WLWH in HIV 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 | 0.001 | 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 | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".