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Mental health resources for older persons in the Western Pacific Region of the World Health Organization

2007· article· en· W2030641207 on OpenAlexaff
Carlos Augusto de Mendonça Lima, Annette Leibing, Rüdiger BUSCHFORT

Bibliographic record

VenuePsychogeriatrics · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de MontréalGroup for Research in Decision Analysis
Fundersnot available
KeywordsLife expectancyWorld populationPopulationMental healthGerontologyHealth carePublic healthDiseasePopulation ageingMedicineGeriatricsPsychologyPsychiatryEnvironmental healthPolitical scienceNursing

Abstract

fetched live from OpenAlex

Abstract Background: The Western Pacific Region of the World Health Organization (WHO) contains 27% of the world population and 29% of the world population of elderly people: it is the oldest among the six WHO regions. The demographic transition (from high child mortality and low life expectancy to a low child mortality and high life expectancy) is happening at high speed and this proportion of the world's elderly population is expected to increase significantly in the coming years. Consequently, there is a high risk that the number of older persons with mental disorders will significantly increase. To better understand the organization of care for older persons, data is being collected to reduce the imbalance between ‘disease information’ and ‘resource information’—information that addresses older persons' needs in terms of mental health care. This article presents some results for this region. Method: Data were essentially collected from the World Health Report 2005. The data concerning NGOs was obtained at the websites of the World Psychiatric Association, the World Federation of Neurology, the International Association of Gerontology and Geriatrics and Alzheimer's Disease International (ADI). Results: Because the mental health problems of older adults is still not a public health priority, the careful examination of individual countries nevertheless reveals certain specificities, such as divergent life expectancy or different values regarding aging. The authors present some recommendations for the development of care for elderly persons with mental disorders in the region based on the general recommendations made by WHO in the World Health Report 2001 (WHR 2001) and by WHO and the World Psychiatric Association (WPA) in some consensus statements on Psychiatry of the Elderly. Conclusion: The Western Pacific WHO region has a high proportion of older persons in its population (11.2% of total population), with important differences between the countries in life expectancy at birth. Strong cultural forces and migration may affect the growing and aging processes. Local poverty, with all the consequences it may have for health, may also be an important factor negatively influencing quality of life and life expectancy at birth. Mental health for all ages should become a priority issue in the public agenda in the region. National governments should make efforts to promote mental health and provide care for elderly persons with mental disorders, depending on local resources and cultures. As mental disorders in old age can represent a severe limitation on quality of life for older persons and their families good care should be extended to all concerned. The challenge of finding solutions for better living conditions for older people with mental health problems lies in the hands of researchers, policy makers and the population. Funding and creativity are two factors necessary to find such solutions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.030
GPT teacher head0.359
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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