Predictors of and Reasons for Early Discharge from an Inpatient Withdrawal Management Service
Bibliographic record
Abstract
Early discharges, also known as against medical advice discharges, are a significant concern in inpatient withdrawal management settings. Early discharges are associated with negative outcomes for patients and are costly for the healthcare system. A recent scoping review found gaps in the literature regarding patient perspectives, and a lack of research exploring predictors of early discharge according to demographic factors such as sex or gender which are known to influence healthcare experiences. It is vital to advance our understanding of patient perceptions of early discharges and what may predict them in order to prevent early discharges from occurring.The aim of this research was to investigate possible predictors of early discharge and patient experiences of early discharge from an inpatient withdrawal management service in Toronto, Ontario, Canada. This aim was accomplished using 2 studies. A retrospective cohort study using administrative health data was conducted to examine independent predictors of early discharge among patients admitted to an inpatient withdrawal management service between 2016 and 2020. Sex-stratified analyses using logistic regression and generalized estimating equations revealed that weekends and younger age were the strongest predictors of early discharge for both males and females. Among females only, being part of an ethnic minority group predicted early discharge. A qualitative descriptive study was conducted to explore patient perceptions of precipitants and processes related to early discharge. Thirteen people who had recently experienced an early discharge participated in the study and reported that precipitants to early discharge included external pressures, concerns about COVID-19, and dissatisfaction. Participants also described hitting a wall or reaching a breaking point prior to early discharge. Further, they often described difficult conversations about their desire to leave early and had mixed perceptions of their early discharge experience. This research demonstrates that there may be opportunities to prevent early discharges by responding to sources of dissatisfaction and exploring contributing factors during high-risk times such as weekends. Further, this research indicates that patient experiences could be improved by modifying processes associated with early discharge when they cannot be prevented.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".