Psychosocial well-being and mental health of low- and middle-income countries’ internally displaced persons and refugees during COVID-19: a systematic literature review
Bibliographic record
Abstract
Background: The COVID-19 pandemic brought to light the need to address the psychosocial and mental health needs of refugees and internally displaced persons in low- and middle-income countries. COVID-19 prevention measures slowed essential services and healthcare, creating unique challenges for refugees and IDPs, including economic insecurity and societal instability. All of these factors may contribute to the reported declines in their psychosocial well-being. Methods: To effectively define the problems of low-and middle-income countries (LMICs) in addressing the needs of these populations, we conducted a systematic literature review of literature on the mental health and psychosocial well-being of refugees and displaced persons who have migrated between LMICs in the context of COVID-19. Findings: Our findings indicate that mental health interventions, such as digital healthcare and community-focused solutions, have the potential to address the problems faced by refugees and IDPs. Nevertheless, these community-based support networks are overextended, continuously developing to meet the needs of these vulnerable populations while considering the limited digital literacy of the subject population, internet accessibility, and overall limits in reach. We found that the efficacy of interventions varied according to the distinctive needs and challenges of various refugee and IDP populations. Implications: The findings indicate a need for an intersectional policy approach to address the complex network of factors influencing mental health outcomes, including gender, housing, employment status, and social inequalities. Global agencies, policymakers, and local governments must prioritize the development of comprehensive mental health support systems, assuring refugees and IDPs have sustainable and equitable access.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".