Cohort study of team-based care among marginalized people who use drugs in Ottawa
Bibliographic record
Abstract
OBJECTIVE: To describe team-based care use among a cohort of people who use drugs (PWUD) and to determine factors associated with receipt of team-based care. DESIGN: A cohort study using survey data collected between March and December 2013. These data were then linked to provincial-level health administrative databases to assess patterns of primary care among PWUD in the 2 years before survey completion. SETTING: Ottawa, Ont. PARTICIPANTS: Marginalized PWUD 16 years of age or older. MAIN OUTCOME MEASURES: Patients were assigned to primary care models based on survey responses and then were categorized as attached to team-based medical homes, attached to non-team-based medical homes, not attached to a medical home, and no primary care. Descriptive statistics and multinomial logistic regression were used to determine associations between PWUD and medical home models. RESULTS: Of 663 total participants, only 162 (24.4%) received team-based care, which was associated with high school level of education (adjusted odds ratio [AOR] = 2.18; 95% CI 1.13 to 4.20), receipt of disability benefits (AOR = 2.47; 95% CI 1.22 to 5.02), and HIV infection (AOR = 2.88; 95% CI 1.28 to 6.52), and was inversely associated with recent overdose (AOR = 0.49; 95% CI 0.25 to 0.94). In comparison, 125 (18.8%) received non-team-based medical care, which was associated with university or college education (AOR = 2.31; 95% CI 1.04 to 5.15) and mental health comorbidity (AOR = 4.18; 95% CI 2.33 to 7.50), and was inversely associated with being detained in jail in the previous 12 months (AOR = 0.51; 95% CI 0.28 to 0.90). CONCLUSION: Although team-based, integrated models of care will benefit disadvantaged groups the most, few PWUD receive such care. Policy makers should mitigate barriers to physician care and improve integration across health and social 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".