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Record W4398331341 · doi:10.3390/ijerph21060664

Optimizing Older Adult Mental Health in Support of Healthy Ageing: A Pluralistic Framework to Inform Transformative Change across Community and Healthcare Domains

2024· article· en· W4398331341 on OpenAlexafffund
Salinda Horgan, Jeanette Prorok, Katie Ellis, Laura Mullaly, Keri-Leigh Cassidy, Dallas Seitz, Claire Checkland

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2024
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of CalgaryDalhousie UniversityMental Health Commission of CanadaQueen's University
FundersCommission de la santé mentale du Canada
KeywordsTransformative learningMental healthConceptual frameworkGlobeHealth careActive ageingPopulation ageingGerontologyPsychologyScale (ratio)Public relationsPopulationSociologyMedicinePolitical scienceOlder peopleEnvironmental healthSocial scienceGeographyPedagogyPsychiatry

Abstract

fetched live from OpenAlex

This paper describes a pluralistic framework to inform transformative change across community and healthcare domains to optimize the mental health of older adults in support of healthy ageing. An extensive review and analysis of the literature informed the creation of a framework that contextualizes the priority areas of the WHO Decade of Health Ageing (ageism, age-friendly environments, long-term care, and integrated care) with respect to older adult mental health. The framework additionally identifies barriers, facilitators, and strategies for action at macro (social/system), meso (services/supports), and micro (older adults) levels of influence. This conceptual (analytical) framework is intended as a tool to inform planning and decision-making across policy, practice, education and training, research, and knowledge mobilization arenas. The framework described in this paper can be used by countries around the globe to build evidence, set priorities, and scale up promising practices (both nationally and sub-nationally) to optimize the mental health and healthy ageing trajectories of older adults as a population.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.154
GPT teacher head0.501
Teacher spread0.347 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations33
Published2024
Admission routes2
Has abstractyes

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