Substance use and its impact on care outcomes among HIV-infected individuals in Manitoba
Bibliographic record
Abstract
The high prevalence of substance use among HIV-infected individuals creates numerous challenges to patient care. This study was undertaken in order to understand the impact of substance use on care outcomes for HIV-infected individuals in Manitoba. Clinical records of 564 HIV-infected individuals in care at Health Sciences Centre in Winnipeg, Manitoba were reviewed. Clinical data were extracted from patient charts for substance users (illicit substance users, alcohol abusers and chronic users of opioids or benzodiazepines) and non-users. Substance users and non-users were analysed using chi-square analysis and logistic regression models to compare basic socio-demographic and clinic variables. Chi-square and analysis of variance were used to compare a subset of substance users based on similar socio-demographic and clinical characteristics. Among HIV-infected individuals in Manitoba, 38% were substance users with over-representation by Aboriginals, females, young adults and residents of Winnipeg's core areas. Opioids and benzodiazepines were the most commonly used substances with the majority of substance users having used multiple classes of substances in their lifetime. Substance users were more likely than non-users to have missed clinic appointments. Among substance users, missed appointments were more common among those who self-identified as Aboriginal, female, young adults, residents of Winnipeg's core areas, heterosexuals and those who had abused alcohol or cocaine/crack. Aboriginal substance users were also less likely to achieve viral load suppression compared to non-Aboriginal substance users. With the high prevalence of substance use among HIV-infected individuals in Manitoba, it is important to identify at-risk individuals in order to implement appropriate care strategies and improve treatment adherence and health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".