Disease burden and mental health system capacity: WHO Atlas study of 117 low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Treatment coverage for mental disorders ranges from less than 10% to more than 90% across low- and middle-income (LAMI) countries. Studies have yet to examine whether the capacity of mental health systems might be adversely affected by the burdens of unrelated conditions such as HIV/AIDS. AIMS: To examine whether the magnitude of disease burden from communicable, perinatal, maternal and nutritional conditions - commonly referred to as Group 1 diseases - is inversely associated with mental health system capacity in LAMI countries. METHOD: Multiple regression analyses were undertaken using data from 117 LAMI countries included in the 2011 World Health Organization (WHO) Mental Health Atlas. Capacity was defined in terms of human resources and infrastructure. Regressions controlled for effects of political stability, government health expenditures, income inequality and neuropsychiatric disease burden. RESULTS: Higher Group 1 disease burden was associated with fewer psychiatrists, psychologists and nurses in the mental health sector, as well as reduced numbers of out-patient facilities and psychiatric beds in mental hospitals and general hospitals (t = -2.06 to -7.68, P<0.05). CONCLUSIONS: Evidence suggests that mental health system capacity in LAMI countries may be adversely affected by the magnitude of their Group 1 disease burden.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".