Integrating mental health services into primary health care – a review of challenges and outcomes in the international setting
Bibliographic record
Abstract
Introduction Mental illness accounts for about one-third of the world’s disability, a burden that many health systems cannot adequately respond to. Up to 70% of mental health (MH) patients are followed-up in primary health care (PHC) settings. To bridge the treatment gap, the World Health Organization developed mhGAP, a guidance package for integrated management of priority MH disorders in lower-income countries. Other countries have developed their own evidence-based interventions. Objectives Overviewing countries’ strategies towards integrating MH services into PHC, their outcomes and challenges. Methods Review of literature using PubMed search terms “mental health primary care”, MeSH terms “Primary Health Care”, “Mental Health Care” and “organization and administration”, published in the last 5 years, in English. Results 25 of 602 articles were selected. The mhGAP programme has seen successful integration in pilot district-level programs, but wider implementation has stalled due to stigma and lack of clinical engagement, resources, MH specialists, and policy support. The Quebec MH reform promoted integrated service networks, improving accessibility and quality of care (QoC). A Norwegian-Russian long-standing collaboration initiative has significantly improved treatment for anxiety and depression (A&D), with 58% reliable recovery rate. A Danish collaborative care intervention provided high-quality treatment of moderate A&D. In Peru, a similar initiative allowed early detection, referral, and treatment of MH patients attending PHC services. Conclusions Comprehensive, integrated and responsive collaborative care models are a cost-efficient strategy to improve QoC for many MH conditions across diverse populations. MH-PHC integration initiatives have seen varying degrees of success. However, several barriers impact wider implementation and scale-up.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".