Interventions to increase help-seeking for mental health care in low- and middle-income countries: A systematic review
Why this work is in the frame
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Bibliographic record
Abstract
Mental health problems are a significant and growing cause of morbidity worldwide. Despite the availability of evidence-based interventions, most people experiencing mental health problems remain untreated. This treatment gap is particularly large in low- and middle-income countries (LMIC) and is due to both supply-side and demand-side barriers. The aim of this systematic review is to identify and synthesise the evidence on interventions to improve help-seeking for mental health problems in LMICs. The protocol was registered a priori (Registration number: CRD42021255635). We searched eight databases using terms based on three concepts: 'mental health/illness' AND 'help-seeking' AND 'LMICs'; and included all age groups and mental health problems. Forty-two papers were eligible and included in this review. Intervention components were grouped into three categories following the steps in the help-seeking process: (1) raising mental health awareness among the general population (e.g., distribution of printed or audio-visual materials), (2) identification of individuals experiencing mental health problems (e.g., community-level screening or detection), and (3) promoting help-seeking among people in need of mental health care (e.g., sending reminders). The majority of interventions (80%) included components in a combination of the aforementioned categories. Most studies report positive outcomes, yet results on the effectiveness is mixed, with a clear trend in favour of interventions with components from more than one category. Ten out of 42 studies (24%) yielded a statistically significant effect of the intervention on help-seeking; and all targeted a combination of the aforementioned categories (i.e., raising awareness, identification and help-seeking promotion). Only six studies (14%) focused on children and adolescents. Due to the limited number of robust studies done in LMICs and the heterogeneity of study designs, outcomes and components used, no definite conclusions can be drawn with regards to the effects of individual strategies or content of the interventions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.000 |
| 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 it