Factors Associated with Leaving Hospital against Medical Advice among People Who Use Illicit Drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Leaving hospital against medical advice (AMA) is common among people who use illicit drugs (PWUD) and is associated with severe health-related harms and costs. However, little is known about the prevalence of and factors associated with leaving AMA among PWUD. METHODS: Data were collected through two Canadian prospective cohort studies involving PWUD between September 2005 and July 2011 and linked to a hospital admission/discharge database. Bivariable and multivariable generalized estimating equations were used to examine factors associated with leaving hospital AMA among PWUD who were hospitalized. RESULTS: Among 488 participants who experienced at least one hospitalization, 212 (43.4%) left the hospital AMA at least once during the study period. In multivariable analyses, factors positively and significantly associated with leaving hospital AMA included: unstable employment (AOR = 1.92; 95% confidence interval [CI]: 1.22-3.03); recent incarceration (AOR = 1.63; 95%CI: 1.07-2.49); ≥ daily heroin injection (AOR = 1.49; 95%CI: 1.05-2.11); and younger age per year younger (adjusted odds ratio [AOR] = 1.04; 95%CI: 1.02-1.06). CONCLUSIONS: We found a substantial proportion of PWUD in this setting left hospital AMA and that various markers of risk and vulnerability were associated with this phenomenon. Our findings highlight the need to address substance abuse issues early following hospital admission. These findings further suggest a need to develop novel interventions to minimize PWUD leaving 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".