Risk factors associated with dropout and readmission among First Nations individuals admitted to an inpatient alcohol and drug detoxification program.
Bibliographic record
Abstract
BACKGROUND: There is a need for clinically relevant research into treatment for substance abuse among Aboriginal people. In this study, I aimed to provide a predictive model of dropout from and readmission to an inpatient detoxification program in a large treatment sample of Aboriginal patients. METHODS: I reviewed the medical charts of all self-reported First Nations people (n = 877) admitted to an inpatient detoxification centre in British Columbia, between Jan. 4, 1999, and Jan. 30, 2002, and used binary logistic regression models to identify predictors of dropout from and readmission to the program. Each of these models was validated using an independent subset of the treatment sample. RESULTS: Overall, 254 (29.0%) people dropped out of the program, and 219 were readmitted. Statistically significant predictors of treatment dropout were a preferred drug other than alcohol (odds ratio [OR] 1.67, 95% confidence interval [CI] 1.12-2.50) and self-referral (OR 1.89, 95% CI 1.28-2.80). Statistically significant predictors of readmission to inpatient detoxification within a 1-year period were a previous history of detoxification treatment (OR 3.52, 95% CI 2.16-5.75) and residential instability (OR 1.82, 95% CI 1.11-2.99). INTERPRETATION: Although factors were identified that are associated with each of treatment dropout or readmission for detoxification, only the latter can be reliably predicted by them.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".