Predictors of Patient-Initiated Discharge From an Inpatient Withdrawal Management Service
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to examine sex-stratified independent predictors of patient-initiated discharge from an inpatient withdrawal management service and to determine whether those predictors differed by sex. METHODS: This study compared people who had self-initiated versus planned discharges and used sex-stratified generalized estimating equations models to identify independent predictors of patient-initiated discharge. Predictors examined included age, ethnicity, substance of concern, tobacco use, mental health comorbidities, day of discharge, referral source, children, and social assistance funds. RESULTS: Among females, there were 722 discharges, 116 of which were patient initiated. Among females, increasing age was associated with lower odds of patient-initiated discharge ( OR = 0.97, 95% CI [0.95, 0.98]). Racialized females were nearly 2 times more likely to experience patient-initiated discharge compared with White females ( OR = 1.8, 95% CI [1.09, 3.00]). Compared with weekdays, weekends were associated with over 4 times the odds of patient-initiated discharge ( OR = 4.77, 95% CI [2.66, 8.56]). Having one or more mental health comorbidities was associated with lower odds of patient-initiated discharge compared with having no mental health comorbidities ( OR = 0.51, 95% CI [0.32, 0.82]). Among males, there were 1,244 discharges, 185 of which were patient initiated. Among males, increasing age was associated with decreased odds of patient-initiated discharge ( OR = 0.97, 95% CI [0.95, 0.98]). Compared with weekdays, weekends were associated with nearly 15 times the odds of patient-initiated discharge ( OR = 14.9, 95% CI [9.11, 24.3]). CONCLUSIONS: Males and females have shared and unique predictors of patient-initiated discharge. Future studies should continue to examine the influence of sex and gender on engagement with addictions care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".