Predictors of and reasons for early discharge from inpatient withdrawal management settings: A scoping review
Bibliographic record
Abstract
ISSUES: Early discharges, also known as 'against medical advice' discharges, frequently occur in inpatient withdrawal management settings and can result in negative outcomes for patients. The purpose of this scoping review is to identify what is known about predictors of and reasons for the early discharge among adults accessing inpatient withdrawal management settings. APPROACH: MEDLINE, CINAHL, PsycINFO, ASSIA and EMBASE were searched, resulting in 2587 articles for screening. Title and abstract screening and full-text review were completed by two independent reviewers. Results were synthesised in quantitative and qualitative formats. KEY FINDINGS: Sixty-two studies were included in this scoping review. All studies focused on predictors of early discharge, except one which only described reasons for the early discharge. Forty-eight percent of studies involved retrospective review of health records data. The most frequently examined variables were demographics. Variables related to the treatment setting, such as referral source and treatment received, were examined less frequently but were more consistently associated with early discharge compared to demographics. Only six studies described patient reasons for the early discharge, which were retrieved via clinical documentation. The most common reasons for early discharge were dissatisfaction with treatment and family issues. IMPLICATIONS AND CONCLUSIONS: Most demographic variables do not consistently predict early discharge, and reasons for early discharge are not well understood. Future studies should focus on the predictive value of non-patient-level variables, or conduct analyses to account for predictors of early discharge among different subgroups of people (e.g. by gender or ethnicity). Qualitative research exploring patient perspectives is needed.
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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 | 0.017 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".