Mental Health Promotion and Risk Reduction Strategies for Mental Disorders in Older Persons: Why Should Governments and Policymakers Care?
Bibliographic record
Abstract
There is no health without mental health. These are both indispensable human rights and are prerequisite to living one's life with dignity. Unfortunately, mental health systems have been in crisis, with burden of mental illness being among the ten leading healthcare-related issues worldwide, with no measurable reduction in such for over 30 years. Concurrently, the demographic clock continues to tick. Toady's 703 million people aged 65 or older are projected to reach 1.5 billion by the year 2050. Of these, 20% will suffer with serious mental health conditions. At the heart of the global crisis for older people is ageism, frequently intersecting with ableism, mentalism, sexism, and racism. These biases result in the violation of older peoples' human rights every day, with the resultant poor quality of life and premature death. They are compounded by major gaps in legislation, policies, and practices, rendering the central transformative promise of the UN's 2030 Agenda to "Leave No One Behind" a very elusive goal. Evidence-based interventions designed to prevent or reduce the risk of common mental health conditions and psychosocial disability are already available. All governments and policymakers have a major role to play in the promotion of good mental health and the prevention of mental illness by integrating these into public health and general social policy. This requires adopting, implementing, and scaling up of evidence-based, cost-effective interventions to reduce the risk of the development of mental disorders and providing access to adequate treatment when needed for older persons. All governments and policymakers also have a pivotal role to play in leading and supporting a UN convention on the human rights of older people. A UN convention would help combat ageism at the national and international levels by ensuring integration of monitoring and enforcement mechanisms to effectively implement policies and laws that could address discrimination, inequity, and the protection of human rights of older people, including their mental health.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".