Methodological approaches to situational analysis in global mental health: a scoping review
Bibliographic record
Abstract
Global inequity in access to and availability of essential mental health services is well recognized. The mental health treatment gap is approximately 50% in all countries, with up to 90% of people in the lowest-income countries lacking access to required mental health services. Increased investment in global mental health (GMH) has increased innovation in mental health service delivery in LMICs. Situational analyses in areas where mental health services and systems are poorly developed and resourced are essential when planning for research and implementation, however, little guidance is available to inform methodological approaches to conducting these types of studies. This scoping review provides an analysis of methodological approaches to situational analysis in GMH, including an assessment of the extent to which situational analyses include equity in study designs. It is intended as a resource that identifies current gaps and areas for future development in GMH. Formative research, including situational analysis, is an essential first step in conducting robust implementation research, an essential area of study in GMH that will help to promote improved availability of, access to and reach of mental health services for people living with mental illness in low- and middle-income countries (LMICs). While strong leadership in this field exists, there remain significant opportunities for enhanced research representing different LMICs and regions.
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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 | 0.125 | 0.246 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.040 | 0.040 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".