The association between neighborhood socioeconomic deprivation and mental health in low- and middle-income countries: A scoping review
Bibliographic record
Abstract
BACKGROUND: Environmental research on mental health primarily originates from high-income countries, while information about the rest of the world remains limited. AIMS: This study examined: (1) the available published research evidence regarding the association between neighborhood-level deprivation and indicators of mental health and illness in low- and middle-income countries (LMICs), and (2) the gaps in the relevant research in LMIC settings that should be addressed in future studies. METHOD: First, we systematically searched for relevant primary studies in electronic databases (Ovid Medline, Scopus, Socindex, and PsycINFO) and citations in the reference lists. Then, a two-stage screening procedure was employed to select the relevant studies by screening the titles and abstracts and reviewing the selected full texts by independent researchers. After charting the data from the selected study reports, we collated, summarized, and discussed the results. RESULTS: = 37) reported a positive association between neighborhood deprivation and mental health/disorder. However, the research methods used varied significantly, and there were several methodological limitations. CONCLUSIONS: This review highlights the need for more original studies in LMICs on the association between neighborhood deprivation and mental health, employing stronger methodologies.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".