Delivering mental health and psychosocial support interventions to women and children in conflict settings: a systematic review
Bibliographic record
Abstract
Background: Over 240 million children live in countries affected by conflict or fragility, and such settings are known to be linked to increased psychological distress and risk of mental disorders. While guidelines are in place, high-quality evidence to inform mental health and psychosocial support (MHPSS) interventions in conflict settings is lacking. This systematic review aimed to synthesise existing information on the delivery, coverage and effectiveness of MHPSS for conflict-affected women and children in low-income and middle-income countries (LMICs). Methods: We searched Medline, Embase, Cumulative Index of Nursing and Allied Health Literature (CINAHL) and Psychological Information Database (PsycINFO)databases for indexed literature published from January 1990 to March 2018. Grey literature was searched on the websites of 10 major humanitarian organisations. Eligible publications reported on an MHPSS intervention delivered to conflict-affected women or children in LMICs. We extracted and synthesised information on intervention delivery characteristics, including delivery site and personnel involved, as well as delivery barriers and facilitators, and we tabulated reported intervention coverage and effectiveness data. Results: The search yielded 37 854 unique records, of which 157 were included in the review. Most publications were situated in Sub-Saharan Africa (n=65) and Middle East and North Africa (n=36), and many reported on observational research studies (n=57) or were non-research reports (n=53). Almost half described MHPSS interventions targeted at children and adolescents (n=68). Psychosocial support was the most frequently reported intervention delivered, followed by training interventions and screening for referral or treatment. Only 19 publications reported on MHPSS intervention coverage or effectiveness. Discussion: Despite the growing literature, more efforts are needed to further establish and better document MHPSS intervention research and practice in conflict settings. Multisectoral collaboration and better use of existing social support networks are encouraged to increase reach and sustainability of MHPSS interventions. PROSPERO registration number: CRD42019125221.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".