Geographic distribution and prevalence of complex health and social needs in British Columbia, Canada
Bibliographic record
Abstract
Background The overlapping challenges of providing care for people with mental illness, substance dependence, homelessness, and involvement with the justice system are becoming increasingly problematic in jurisdictions throughout the world. Individuals with complex presentations including health and socio-legal needs have been overwhelmingly identified in urban settings where the majority of interventional research has also taken place. However, little is known about the geographic distribution of individuals with similarly complex needs over a large population base that includes urban, rural and remote settings. The goal of this study was to empirically measure the prevalence and geographic distribution of people who experience complex co-occurring disorders (CCD) in a Canadian Province to provide a basis for planning and delivery of indicated services and supports. Methods Linked administrative data were examined spanning health, income assistance, and criminal convictions within a population of approximately 3.7 million adults and over a five-year period. Inclusion criteria were: diagnosed substance use and mental disorders; psychiatric hospitalization; criminal conviction; and income assistance. Geographic distribution was examined by calculating the prevalence per 100,000 adults examined at four geographic levels of increasing size. Results 2,202 individuals (1.2% of the population) met our inclusion criteria for CCD. In addition to having concurrent substance use and mental disorders, participants had high rates of hospitalization (8.2 admissions), criminal convictions (8.6 sentences) and social assistance payments (>$36,000) within five years. There was wide variability in the geographic distribution of people with CCD. Conclusions People with CCD are not restricted to areas with large populations or to urban settings. While higher absolute numbers of CCD individuals were identified in regions with higher overall populations, the highest rates of CCD were observed in relatively remote locations. Empirically-supported services for those with CCD (e.g., specialized courts, assertive community treatment) are needed in urban areas, but these services must also be adapted for those non-urban settings where the prevalence of CCD is greatest. Key messages Individuals with complex, co-occurring disorders are likely to present in diverse regions. Effective models of inter-agency collaboration used in urban settings are also required in less populated environments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".