Hospitalization for mental health related ambulatory care sensitive conditions: what are the trends for First Nations in British Columbia?
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples globally experience a disproportionate burden of mental illness due to forced policies and practices of colonization and cultural disruption. The objective of this study was to provide a baseline profile of hospitalization rates for mental health-related Ambulatory Care Sensitive Conditions among First-Nations living both on and off reserve in British Columbia, Canada, and explore the relationship between local access to health services and mental health-related hospitalization rates. METHODS: A population-based time trend analysis of mental health-related Ambulatory Care Sensitive Conditions hospitalizations was conducted using de-identified administrative health data. The study population included all residents eligible under the universal British Columbia Medical Services Plan and living on and off First Nations reserves between 1994/95 and 2009/10. The definition of mental health-related Ambulatory Care Sensitive Conditions included mood disorders and schizophrenia, and three different change measures were used to operationalize avoidable hospitalizations: 1) rates of episodes of hospital care, 2) rates of length of stay, and 3) readmission rates. Data were analyzed using generalized estimating equations approach, controlling for age, sex, and socio-economic status, to account for change over time. RESULTS: Our findings show that First Nations living on reserve have higher hospitalization rates for mental disorders compared to other British Columbia residents up until 2008. Those living off reserve had significantly higher hospitalization rates throughout the study period. On-reserve communities served by nursing stations had the lowest rates of hospitalization whereas communities with limited local services had the highest rates. Compared to other British Columbia residents, all First Nations have a shorter length of stay and lower readmission rates. CONCLUSIONS: This study suggests that despite reduced rates of hospitalization for mental-health related Ambulatory Care Sensitive Conditions over time for First Nations, gaps in mental health care still exist. We argue greater investments in primary mental health care are needed to support First Nations health. However, these efforts should place equal importance on prevention and the social determinants of health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".