Profiles of quality of outpatient care use, associated sociodemographic and clinical characteristics, and adverse outcomes among patients with substance-related disorders
Bibliographic record
Abstract
BACKGROUND: This study identified patient profiles in terms of their quality of outpatient care use, associated sociodemographic and clinical characteristics, and adverse outcomes based on frequent emergency department (ED) use, hospitalization, and death from medical causes. METHODS: A cohort of 18,215 patients with substance-related disorders (SRD) recruited in addiction treatment centers was investigated using Quebec (Canada) health administrative databases. A latent class analysis was produced, identifying three profiles of quality of outpatient care use, while multinomial and logistic regressions tested associations with patient characteristics and adverse outcomes, respectively. RESULTS: Profile 1 patients (47% of the sample), labeled "Low outpatient service users", received low quality of care. They were mainly younger, materially and socially deprived men, some with a criminal history. They had more recent SRD, mainly polysubstance, and less mental disorders (MD) and chronic physical illnesses than other Profiles. Profile 2 patients (36%), labeled "Moderate outpatient service users", received high continuity and intensity of care by general practitioners (GP), while the diversity and regularity in their overall quality of outpatient service was moderate. Compared with Profile 1, they were older, less likely to be unemployed or to live in semi-urban areas, and most had common MD and chronic physical illnesses. Profile 3 patients (17%), labeled "High outpatient service users", received more intensive psychiatric care and higher quality of outpatient care than other Profiles. Most Profile 3 patients lived alone or were single parents, and fewer lived in rural areas or had a history of homelessness, versus Profile 1 patients. They were strongly affected by MD, mostly serious MD and personality disorders. Compared with Profile 1, Profile 3 had more frequent ED use and hospitalizations, followed by Profile 2. No differences in death rates emerged among the profiles. CONCLUSIONS: Frequent ED use and hospitalization were strongly related to patient clinical and sociodemographic profiles, and the quality of outpatient services received to the severity of their conditions. Outreach strategies more responsive to patient needs may include motivational interventions and prevention of risky behaviors for Profile 1 patients, collaborative GP-psychiatrist care for Profile 2 patients, and GP care and intensive specialized treatment for Profile 3 patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".