Internalizing Disorder among Individuals with Substance Use Disorders: A Systematic Review
Bibliographic record
Abstract
A strong bidirectional relationship exists between substance use disorders (SUDs) and depression, with several factors contributing to their co-occurrence. The systematic review was conducted from June 2021 to December 2021, covering a span of five years and comprised search on PsycINFO, EMBASE, Google Scholar, MEDLINE, PubMed, Web of Science, Science Direct, Clinical Trials.gov, and OvidSP. Out of the initial 314 studies that were identified, a total of 41 (13%) underwent a comprehensive full-text assessment. Among 30 studies, 16 (53 %) were conducted in USA, 4 (13%) in France, 2 (7%) in Canada, and 1(3%) each in Spain, India, China, Egypt, Greece, Ghana, Italy, and Norway. The studies indicated that individuals with depression were more likely to have a co-occurring substance use disorder or vice versa. This systematic review provides evidence of the substantial impact of internalizing disorder on individuals with SUDs, emphasizing the significance of integrated interventions addressing both mental health and substance use concerns.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| 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".