Characteristics of Adults With Addictions and Mental Health Problems Who Have Experienced Homelessness: A Population-Based Study From Alberta, Canada: Caractéristiques des adultes aux prises avec des problèmes de dépendance et de santé mentale et ayant connu l’itinérance : une étude fondée sur la population de l’Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: This study seeks to understand the characteristics of individuals with addictions and other mental health (AMH) conditions who had a history of homelessness compared to those who did not experience homelessness. METHOD: This cross-sectional analysis used linked administrative data from Alberta, Canada on April 1, 2018. People with AMH who experienced homelessness in the year prior to index were identified using hospitalisations and emergency department (ED) visits. We used multivariable logistic regression to evaluate the association between a set of descriptive variables and homelessness, adjusted for age and sex. RESULTS: < .001) than individuals not experiencing homelessness. PEH were also more likely to be diagnosed with multiple AMH disorders (44.8% diagnosed with ≥ 4 AMH conditions vs. 3.8% of individuals without homelessness). PEH were more likely to have a history of visiting a psychiatrist (adjusted odds ratio (AOR) = 8.11, 95% CI [7.47-8.80], having an ED visit for AMH reasons (AOR = 25.44, 95% CI [22.94-28.21], and to have been hospitalised for AMH reasons (AOR = 13.53, 95%CI [12.61-14.52]). CONCLUSIONS: Within the population of individuals with diagnosed AMH conditions, PEH demonstrated increased AMH complexity, greater healthcare utilisation and a greater likelihood of almost all AMH disorders. Given the complex mental health needs of this group, they will require more intensive mental health and general medical services that must be integrated with housing and additional social support systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".