MétaCan
Menu
Back to cohort
Record W2730626706 · doi:10.1093/geroni/igx004.2633

DEVELOPMENT OF A PERSON-CENTERED INDEX OF AGING WELL IN CANADA AND MEXICO

2017· article· en· W2730626706 on OpenAlexaffabout
Roberto Carlos Castrejón‐Pérez, Emmanuelle Bélanger, U. Perez-Zepeda, Marı́a Victoria Zunzunegui

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychosocialMoodGerontologyIndex (typography)PsychologyCognitionInternational Classification of Functioning, Disability and HealthSuccessful agingFocus groupMedicineClinical psychologyRehabilitationPsychiatrySociology

Abstract

fetched live from OpenAlex

Starting from the results of a qualitative study in Canada and existing literature in different contexts, an index capturing most of the domains used in definitions of successful aging was constructed, with a particular focus on lay perspectives. The index was developed using data gathered in 2014 from 663 Canadian participants in the International Mobility in Aging Study (IMIAS) and 15,698 Mexican participants in the Mexican Healthy Aging Study (MHAS). It was computed as a continuous score (0 to 100) containing health-related components (ADL disability, mobility disability, pain, self-rated health, and cognitive function), and psychosocial components (mood, productive activities, social relations, life satisfaction, and self-mastery). Mean scores on aging well were 80 among Canadian older adults and 59 among Mexican participants. The detailed scores indicate aspects of the health and psychosocial well-being that deserve improvement according to older adults themselves, particularly pain management in Canada and mobility disability in Mexico.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.055
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.007
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.220
Teacher spread0.198 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInnovation in AgingSame topicOffshore Engineering and TechnologiesFrench-language works237,207