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Record W3039570905 · doi:10.1177/0898264320932777

A Longitudinal Study on Multidimensional Resilience to Physical and Psychosocial Stress in Elderly Mexicans

2020· article· en· W3039570905 on OpenAlexaff
Jan Höltge, Rafael Samper‐Ternent, Carmen García‐Peña, Luis Miguel Gutiérrez‐Robledo

Bibliographic record

VenueJournal of Aging and Health · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
FundersNational Institute on AgingSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsContext (archaeology)Psychological resiliencePsychosocialDepression (economics)Resistance (ecology)PsychologyLongitudinal studyActivities of daily livingGerontologyCognitionMedicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Objectives: To identify trajectories of depression and daily disability in the context of serious falls and widowhood and to predict those trajectories before the events occurred. Methods: Longitudinal data were used from the Mexican Health and Aging Study. Trajectories were estimated using latent class growth analysis. Internal and socio-ecological resources were analyzed as predictors of the trajectories. Results: Unfavorable (worsening of symptoms and chronic high symptoms) and favorable (improvement of symptoms and stable low symptoms (resistance)) trajectories were identified. Favorable trajectories were more likely for daily disability. Persons who showed resistance in depression also tended to show resistance in daily disability. Net worth, cognition, and subjective well-being were early predictors for most trajectories. Discussion: Besides resistance, individuals rather show different co-occurring trajectories in the studied outcomes. While some factors could be identified that lead to favorable trajectories in both stressful contexts, the study also shows the necessity for context-specific research and praxis.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.464
Teacher spread0.384 · 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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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