The impact of an HIV/AIDS adult integrated health program on leaving hospital against medical advice among HIV-positive people who use illicit drugs
Bibliographic record
Abstract
Background: Leaving hospital against medical advice (AMA) is a major source of avoidable morbidity, mortality and healthcare expenditure. The objective of this study was to assess the impact of an innovative HIV/AIDS adult integrated health program on leaving hospital AMA among HIV-positive people who use illicit drugs (PWUD). Methods: Using generalized estimating equations, we examined the relationship between being a participant of the Dr. Peter Centre (DPC), a specialty HIV/AIDS-focused adult integrated health program, and leaving hospital AMA among a cohort of HIV-positive PWUD patients. Results: Between July 2005 and July 2011, 181 HIV-positive PWUD who experienced ≥1 hospitalization were recruited into the study. Of the 406 hospital admissions among these individuals, 73 (39.9%) participants left the hospital AMA. In a multivariable model adjusted for confounders, being a participant of the DPC was independently associated with lower odds of leaving hospital AMA (adjusted odds ratio = 0.42; 95% confidence interval: 0.19-0.89). Conclusions: Our findings suggest that the provision of a broad range of clinical, harm reduction and support services through an innovative HIV/AIDS-focused adult integrated health program operating in proximity to a hospital may curb the rate at which individuals leave hospital prematurely.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".