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Record W4282832785 · doi:10.1177/01640275221108502

Ageing in Context: An Ecological Model to Understand Social Participation Among Indigenous Adults in Chile

2022· article· en· W4282832785 on OpenAlexaff
Lorena Gallardo-Peralta, Émilie Raymond, José Luis Gálvez-Nieto

Bibliographic record

VenueResearch on Aging · 2022
Typearticle
Languageen
FieldMedicine
TopicAging, Health, and Disability
Canadian institutionsUniversité Laval
FundersFondo Nacional de Desarrollo Científico y Tecnológico
KeywordsIndigenousSocial engagementContext (archaeology)AutonomySocial environmentFeelingSocial supportPsychologyGerontologyEcologyGeographySociologySocial psychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The social participation of older adults occupies a central position in international discourse regarding ageing, the ecological model makes it possible to examine and assess the different factors that influence the understanding of what encourages social participation by older adults. This study used the ecological model to analyse how personal, community and environmental factors are related to satisfaction with social participation among Chilean older adults, a majority of whom are indigenous, living in rural areas ( n = 800). The results confirmed that satisfaction with social participation was related to personal factors (feelings of depression, functioning into basic activities of daily living (ADL) and autonomy), community factors (perceived social support from social group) and environmental factors (accessibility of physical setting within the village). Our findings confirmed a high level of social participation among indigenous adults, with rural and indigenous surroundings appearing to be a factor that protects and promotes social integration.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
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 head0.222
GPT teacher head0.482
Teacher spread0.260 · 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 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

Citations15
Published2022
Admission routes1
Has abstractyes

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