The Epidemiological Profile of Patients with Mental Health Disorders in Opioid Agonist Treatment, Accessing Services in Primary Care across Ontario, Canada
Bibliographic record
Abstract
Objective: To examine the epidemiological profile and health service use patterns of patients enrolled in opioid agonist treatment diagnosed with mental health disorders who access mental health services in a primary care setting across Ontario, Canada.Methods: We conducted a cross sectional study using secondary data from administrative health databases between January 1, 2008, and March 31, 2014.The data was collected in the province of Ontario and patients were stratified on the bases of place of residence into two groups -Northern and Southern Ontario.Descriptive statistics and standardized differences were calculated between Northern and Southern cohorts.Outcomes of interest included: primary care visits, hospitalizations and emergency department visits.Results: We identified 19,458 individuals with a mental health diagnosis and opioid use disorder (OUD/MH) between 2008 and 2014.Of the OUD cohort, 13.4% resided in Northern regions and 87.06% resided in Southern regions of Ontario.The mean number of mental health related visits in Northern Ontario was 38.31 (SD ± 36.03); and Southern Ontario 36.02 (SD ± 30.60) (p<0.05).The average emergency department visits in Northern Ontario was 2.29, SD ± 3.87, and Southern Ontario, 2.10 ± 4.36 (p 0.039); and hospitalizations in Northern Ontario 0.24 SD ± 0.68, versus Southern Ontario, 0.21 ± 0.74 (p 0.05). Conclusions:We believe that our findings can ultimately help in the planning and policy decisions relating to opioid use disorder in across geographies.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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".