Managing the impact of mental health conditions on the cascade of care among women living with HIV
Bibliographic record
Abstract
Background: Mental health issues negatively impact engagement across the HIV care cascade.Women with HIV are more likely to experience worse overall mental health than men with HIV and are consequently at higher risk for poor HIV care outcomes.Given the synergistic interaction of HIV and mental illness, mental health interventions may have implications for HIV care.Specifically, the use of mental health services may improve the engagement of women across the HIV care cascade in addition to promoting mental well-being.Objectives: The first manuscript aims to describe the movement of women with mental health symptoms or diagnoses across the HIV care cascade and determine the association between the use of mental health services and engagement in care cascade steps.The second manuscript describes the availability and use of mental health services and identifies characteristics associated with reporting shortages of these services that present a problem for their care.Methods: Data (CHIWOS) were analyzed.CHIWOS followed 1422 women with HIV over three waves at 18month intervals between 2013 and 2018.For the first manuscript, participants were included if they had clinically significant depressive symptoms or reported a mental health diagnosis at baseline.Four HIV care states were defined among this group at baseline, 18 months, and 36 months: (1) unengaged in care (no contact with an HIV provider in the last year), ( 2) not on antiretroviral therapy (ART) (engaged in care but did not report ART use), ( 3) detectable (reported ART use and a detectable viral load), and ( 4) optimal (reported ART use and an undetectable viral load).Sankey diagrams were constructed to depict the trajectory of women over the waves, and multinomial regression models were used to assess the relationship between mental health service use and care state at baseline.In the second manuscript, descriptive statistics were used to analyze availability and their use of mental health services at HIV clinics and alternate sites.Logistic regression models were employed to determine associations between baseline characteristics and reported problematic shortages of mental health services.Results: 898 participants (63.2% of the cohort) had significant depressive symptoms or mental health diagnoses at baseline, of which 33% reported using mental health services.In our adjusted Impact of Mental Health Service Use on the HIV Care Cascade Among Women
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".