FACTORS ASSOCIATED WITH READMISSIONS OF PATIENTS WITH MENTAL AND SUBSTANCE USE DISORDERS
Bibliographic record
Abstract
ABSTRACT Objective: To analyze the factors associated with the readmissions of patients with mental and substance use disorders. Method: This is a quantitative, cross-sectional, and analytical study. Participants were enrolled from one general hospital, one university hospital and an Emergency Care Unit in the metropolitan area of Porto Alegre, RS, Brazil. The instrument used was the interRAI Emergency Screener for Psychiatry (interRAI ESP). Descriptive analysis and logistic regression were performed to compare the samples and identify factors associated with the risk of multiple readmissions (four or more). Results: The total sample consisted of 324 patients (average age: 41.79 ± 14.27 years, 61.04 % male), with four main diagnoses: mood disorders (29.14 %), substance use disorders (27.70 %), schizophrenia (23.74 %), and anxiety disorders (19.42 %). Most individuals had previous admissions, primarily at university hospital (81.82 %, p = 0.006), but no significant difference was found regarding multiple previous admissions (four or more), which ranged from 34 % to 40 % across data collection sites. A diagnosis of substance-related disorders was the factor most strongly associated with multiple readmissions (OR = 2.75; p = 0.039), followed by behavioral problems (OR = 2.62; p = 0.001) and mania (OR = 2.28; p = 0.012). The item on intrusive thoughts or previous trauma showed an odds ratio of 2.08 (p = 0.016). The presence of family support and community support networks had a protective effect, possibly preventing readmissions (OR = 0.49; p = 0.038). Conclusion: Considering that the main risk factor for readmissions is substance use disorders, there is an urgent need for investments in the Psychosocial Care Centers for alcohol and other drug users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".