Including culture in programs to reduce stigma toward people with mental disorders in low- and middle-income countries
Bibliographic record
Abstract
Stigma is one of the main barriers for the full implementation of mental health services in low- and middle-income countries (LMICs). Recently, many initiatives to reduce stigma have been launched in these settings. Nevertheless, the extent to which these interventions are effective and culturally sensitive remains largely unknown. The present review addresses these two issues by conducting a comprehensive evaluation of interventions to reduce stigma toward mental illness that have been implemented in LMICs. We conducted a scoping review of scientific papers in the following databases: PubMed, Google Scholar, EBSCO, OVID, Embase, and SciELO. Keywords in English, Spanish, and Portuguese were included. Articles published from January 1990 to December 2017 were incorporated into this article. Overall, the studies were of low-to-medium methodological quality-most only included evaluations after intervention or short follow-up periods (1-3 months). The majority of programs focused on improving knowledge and attitudes through the education of healthcare professionals, community members, or consumers. Only 20% (5/25) of the interventions considered cultural values, meanings, and practices. This gap is discussed in the light of evidence from cultural studies conducted in both low and high income countries. Considering the methodological shortcomings and the absence of cultural adaptation, future efforts should consider better research designs, with longer follow-up periods, and more suitable strategies to incorporate relevant cultural features of each community.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".