Predicting Mental Health Hospitalizations Among First Nations Adults in Residential Treatment
Bibliographic record
Abstract
Indigenous peoples in Canada experience disproportionately higher rates of hospitalization for mental health and substance use concerns than non-Indigenous populations. Ambulatory care sensitive conditions are community-level markers of mental illness which can theoretically be managed through community-based health services, and therefore should not require urgent care services. Previous mental health related ambulatory sensitive conditions have included psychotic and mood disorders; however, limited research has explored these relationships of increased hospitalization among specific health disorders with Indigenous populations. This study analyzed predictors of prior mental health hospitalizations among a predominately First Nations population seeking residential treatment for substance use. We hypothesized that increased co-morbid mental health concerns would predict increased hospitalizations for mental health concerns, and this relationship would be moderated by higher Adverse Childhood Experience (ACE) scores. Logistic regression showed that for every increase in each reported mental health concern, the odds of prior hospitalization was 1.6 times higher among this sample, although ACEs did not moderate this relationship. Participants with depressive, anxiety-related, psychotic, and personality-related disorders reported proportionally more hospitalization. Although study results suggest ACEs are not a useful predictor of hospitalization, their presence has previously shown to exacerbate co-morbid concerns, and thus, may influence prior hospitalization history indirectly. Further research can explore relationships between exposure to childhood trauma and hospitalization, particularly in consideration of access to tertiary mental health services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".