Correlates of residential detoxification completers and non-completers in Alberta
Bibliographic record
Abstract
OBJECTIVES: We aimed to identify factors correlated with completion, non-completion and cycling between completion and non-completion among clients admitted to Alberta Residential Withdrawal Management Services. METHODS: The study included data on clients from a provincial database (N = 20,020) admitted to residential withdrawal management units across Alberta between April 1, 2015, and March 31, 2022. We collapsed admission-level data into client-level data, where each individual's information is captured as a unique record. Multinomial logistic regression was performed to investigate correlates of completion, non-completion, and multiple cycles of completion and non-completion. Clients in these three categories were compared in their socio-demographics, primary substance of concern, and other program variables of admission count, length of stay, transition to treatment, facilities utilized, days of discharge, and years of admission. RESULTS: The study sample included 39,952 admissions, with an average of two per client. Overall, 55.8% (n = 11,170) of discharged clients completed the program, 25.5% (n = 5,106) were non-completed, and 18.7% (n = 3,744) cycled between completion and non-completion. Regression analysis indicated that clients who used a single substance, primarily alcohol, cocaine and marijuana, completed post-secondary education, were employed or had unstable employment, and were married had lower odds of non-completion. Other variables related to higher odds of completion were urban residence, multiple admissions to a facility, and longer lengths of stay. Conversely, clients who primarily used amphetamines, barbiturates, crystal meth, opiates, and tranquillizers had higher odds of non-completion. Female gender, being discharged on Saturday and Sunday, using detoxification as a standalone service without transitioning to residential treatment and admission to two or more facilities also correlated with higher odds of non-completion. CONCLUSION: Results indicated that the type of substances, gender, education, marital status, employment, place of residence, and transition to residential services were associated with detoxication outcomes. These findings can inform the customization and allocation of services, targeted support, service intensity and areas requiring additional attention and investment to improve treatment outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".