Mental health service use and shortages among a cohort of women living with HIV in Canada
Bibliographic record
Abstract
BACKGROUND: The prevalence of mental health conditions among women with HIV in Canada ranges between 29.5% and 57.4%, highlighting the need for accessible mental health care. We aimed to (1) describe the availability and use of mental health services among women with HIV and (2) identify characteristics associated with reporting that shortages of these services presented a problem in their care. METHODS: Baseline data from the Canadian HIV Women's Sexual and Reproductive Health Cohort Study were analysed. Self-reported availability and use of mental health services were examined using descriptive statistics. Participants indicated whether a lack of mental health support was a problem in their care. Logistic regression models were constructed to determine associations between sociodemographic, clinical, and psychosocial characteristics and reported problematic shortages. RESULTS: Of 1422 women, 26.7% (n = 380) used mental health services in the last year, which most accessed through their HIV clinic. Thirty-eight percent (n = 541) reported that a shortage of mental health support was a problem in their care. Among this subset, 22.1% (n = 119) used services at their HIV clinic, 26.5% (n = 143) reported available services but did not use them, and 51.4% (n = 277) either indicated that these services were unavailable, did not know if such services were available, or were unengaged in HIV care. Factors associated with reporting problematic shortages included rural residence [adjusted odds ratio (aOR): 1.69, 95% confidence interval (CI): 1.03-2.77], higher education level (aOR: 1.43, 95% CI: 1.02-2.02), and higher HIV stigma score (aOR: 1.03, 95% CI: 1.02-1.03). Conversely, African/Caribbean/Black identity (aOR: 0.37, 95% CI: 0.26-0.54), history of recreational drug use (aOR: 0.56, 95% CI: 0.39-0.81), and Quebec residence (aOR: 0.69, 95% CI: 0.50-0.96) were associated with lower odds of reporting service shortages. CONCLUSION: Our findings highlight the HIV clinic as the primary location of mental health service use. However, existing services may not be sufficient to reach all patients or meet specific needs. Furthermore, the low uptake among those reporting a shortage suggests a lack of connection to services or patient knowledge about their availability. Characteristics associated with reporting shortages reflect geographic and socioeconomic disparities that must be accounted for in future service design.
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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 | 0.003 |
| Science and technology studies | 0.003 | 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".