Risk and protective factors for rehospitalization among substance use disorders: A systematic review
Bibliographic record
Abstract
Introduction: Substance Use Disorder (SUD) is associated with high relapse rates and multiple hospitalizations.Several factors influence detoxification and treatment outcomes, representing a potential risk for relapse and rehospitalization.Objective: This systematic review aimed to explore and summarize the existing research on risk and protective factors for relapse and rehospitalization.Methods: Search terms were applied in different databases: Embase, PubMed, and Web of Science.Three independent researchers performed blind assessments of study eligibility and quality.To assess for risk of bias, we utilized an adapted version of the Newcastle-Ottawa Scale.Results: Forty studies were included suggesting a significant number of risk factors for rehospitalization, including psychiatric comorbidities, psychological trauma exposure, failed program, history of rehospitalization, history and patterns of drug use, family and social problems, occupational status, sex, medical condition, age, ethnicity, and housing.We also described some common protective factors: adherence to treatment, social and familial support, self-efficacy, and characteristics of self.Conclusion: Our findings suggest these variables could affect a person with SUD in withdrawal management and treatments.Clinicians should pay attention to these factors during the assessment phase to orientate interventions to minimize potential risk factors and promote preventive strategies.
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 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".