Effectiveness of Training Non‐Specialist Health Workers for the Management of Substance Use Disorders: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ISSUES: Substance use disorders (SUD) pose a global health challenge, especially in resource-limited settings. Training non-specialist health workers (NSHW) is a promising task-sharing strategy. This systematic review assessed the effectiveness of such training in improving NSHWs' knowledge, attitudes, self-efficacy and practice. APPROACH: We searched PubMed, SCOPUS and EMBASE and included randomised controlled trials (RCT), quasi-experimental and pre-post studies published between 1 January 2008 and 30 October 2024. Data were synthesised using effect sizes reported as standardised mean differences (SMD). Risk of bias assessments included Cochrane Risk of Bias 2.0 (RoB2) for RCTs and the Newcastle-Ottawa Scale for observational studies. KEY FINDINGS: Of 21,892 records, 29 studies were included: RCTs (n = 7), quasi-experimental (n = 4) and pre-post studies (n = 18). Training formats ranged from 30-min sessions to two-year programs, using hybrid approaches with didactics, role-plays and simulations. Content included SBIRT (n = 8), motivational interviewing (n = 5), and management of alcohol, opioid and tobacco use. The pooled analyses of RCTs showed significant short-term changes to NSHW's knowledge [SMD 0.59 (95% CI 0.40, 0.79)], self-efficacy [SMD 0.45 (95% CI 0.26, 0.64)] and practice [SMD 0.24 (95% CI 0.03, 0.46)]. Pooled analysis from RCTs for changing attitude showed a non-significant result. Quality was mixed: most (4/7) RCTs raised some concerns, and observational studies were mostly moderate (6/13), with frequent reliance on self-report, limited confounding control and short follow-up. IMPLICATIONS AND CONCLUSION: Training NSHWs has modest effects on knowledge and self-efficacy and small effects on the practice of SUD management.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".