Validated Screening Tools for Common Mental Disorders in Low and Middle Income Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: A wide range of screening tools are available to detect common mental disorders (CMDs), but few have been specifically developed for populations in low and middle income countries (LMIC). Cross-cultural application of a screening tool requires that its validity be assessed against a gold standard diagnostic interview. Validation studies of brief CMD screening tools have been conducted in several LMIC, but until now there has been no review of screening tools for all CMDs across all LMIC populations. METHODS: A systematic review with broad inclusion criteria was conducted, producing a comprehensive summary of brief CMD screening tools validated for use in LMIC populations. For each validation, the diagnostic odds ratio (DOR) was calculated as an easily comparable measure of screening tool validity. Average DOR results weighted by sample size were calculated for each screening tool, enabling us to make broad recommendations about best performing screening tools. RESULTS: 153 studies fulfilled our inclusion criteria. Because many studies validated two or more screening tools, this corresponded to 273 separate validations against gold standard diagnostic criteria. We found that the validity of every screening tool tested in multiple settings and populations varied between studies, highlighting the importance of local validation. Many of the best performing tools were purposely developed for a specific population; however, as these tools have only been validated in one study, it is not possible to draw broader conclusions about their applicability in other contexts. CONCLUSIONS: Of the tools that have been validated in multiple settings, the authors broadly recommend using the SRQ-20 to screen for general CMDs, the GHQ-12 for CMDs in populations with physical illness, the HADS-D for depressive disorders, the PHQ-9 for depressive disorders in populations with good literacy levels, the EPDS for perinatal depressive disorders, and the HADS-A for anxiety disorders. We recommend that, wherever possible, a chosen screening tool should be validated against a gold standard diagnostic assessment in the specific context in which it will be employed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".