Why mental health service delivery needs to align alongside mainstream medical services
Bibliographic record
Abstract
There is significant individual human suffering and economic burden because of untreated mental health and substance use disorders. There is high psychiatric morbidity in primary and secondary medical care. At least one-fifth of patients attending primary care services in western countries pertain to mental health and one-third of patients attending general hospitals have a comorbid mental disorder. Patients with mental disorders have lower life expectancy than the general population due to various medical conditions and reduced access to physical healthcare. There is a suicide every 40 seconds and the vast majority of those who die by suicide have a diagnosable mental disorder. Despite this, most countries spend less than 2% of their health budgets on mental health. Effective treatments exist for mental disorders, however underfunding, poor integration of services, lack of trained health care professionals and stigma often prevent access to effective treatments. Stigma is a significant barrier to seeking help and receiving treatment. Geographical separation of mental health services from general hospital settings may be perpetuating the stigma of mental illness among the population. In this article, we review the key reasons why mental health services globally need to align with mainstream healthcare services and the longstanding reasons that necessitate the need to make mental health a public health priority.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".