Acute care utilization and its associated determinants among patients with substance‐related disorders: A worldwide systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Identifying determinants of emergency department (ED) use and hospitalization among patients with substance-related disorders (SRDs) can improve health services to address unmet health needs. AIM: The present study aimed to identify the prevalence rates of ED use and hospitalization, and their associated determinants among patients with SRDs. METHODS: Studies in English published from January 1, 1995, to December 1, 2022, were searched on PubMed, Scopus, Cochrane Library, and Web of Science to identify primary studies. RESULTS: The pooled prevalence rates of ED use and hospitalization among patients with SRDs were 36% and 41%, respectively. Patients with SRDs who were the most at risk of being both ED users and hospitalized were those (i) having medical insurance, (ii) having other drug and alcohol use disorders, (iii) having mental health disorders, and (iv) having chronic physical illnesses. A lower level of education increased the risk of ED use only. DISCUSSION: To decrease ED use and hospitalization, more comprehensive services may be offered to these vulnerable patients with diversified needs. IMPLICATIONS FOR PRACTICE: Chronic care integrating outreach interventions could be more provided for patients with SRDs after discharge from acute care units or hospitals.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".