Effect of Case Management Interventions for Patients with Substance Use Disorders: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Substance use disorder (SUD) is an important health problem that requires a complex range of care because of the chronic nature of the disorder and the multiple psychosocial problems involved. Current outpatient programs often have difficulties in delivering and coordinating ongoing care and access to different health-care providers. Various case management (CM) models have been developed, first for patients in other psychiatric domains and then for patients with SUD, in order to improve treatment outcomes. AIM: This paper aims to assess the effectiveness of CM for patients with SUD. METHODS: We performed a systematic review of CM interventions for patients with SUD by analyzing randomized controlled studies published on the subject between 1996 and 2016 found on the electronic database PubMed. RESULTS AND CONCLUSION: Fourteen studies were included in the analysis. Differences between studies in outcome measures, populations included, and intervention characteristics made it difficult to compare results. Most of these studies reported improvement in some of the chosen outcomes. Treatment adherence mostly improved, but substance use was reported to decrease in only a third of the studies. Overall functioning improved in about half of the studies. The heterogeneity of the results might be linked to these differences between studies. Further research is needed in the field.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".