Serious mental health conditions and exposure to adulthood trauma in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
Background: There is a strong link between trauma exposure and serious mental health conditions (SMHCs), such as schizophrenia and bipolar disorder. The majority of research in the field has focused on childhood trauma as a risk factor for developing an SMHC and on samples from high-income countries. There is less research on having an SMHC as a risk factor for exposure to traumatic events, and particularly on populations in low- and middle-income countries (LMICs).This scoping review aimed to synthesize the nature and extent of research on traumatic events that adults with SMHCs face in LMICs. It was conducted across five databases: PubMed, Embase, PsycINFO, Web of Science Core Collection and Africa-Wide Information/NiPad in December 2023 and by hand searching citation lists. Findings: The database search returned 4,111 articles. After removing duplicates and following a rigorous screening process, 51 articles met criteria for inclusion. There was one case study, one mixed methods study, 12 qualitative studies and 37 quantitative studies. Ten countries were represented, with the most studies from India (n = 19), Ethiopia (n = 9) and China (n = 6). Schizophrenia was the most studied type of SMHC. Of the trauma exposures, more than 76% were on interpersonal violence, such as sexual and physical violence. Of the studies on interpersonal violence, more than 23% were on physical restraint (e.g., shackling) in the community or in hospital settings. There were no studies on man-made or natural disasters. Implications: Much of our data in this population are informed by a small subset of countries and by certain types of interpersonal violence. Future research should aim to expand to additional countries in LMICs. Additional qualitative research would likely identify and contextualize other trauma types among adults with SMHCs in LMICs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".