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Record W2323090503 · doi:10.1177/0008417414552188

Coping strategies associated with participation and quality of life in older adults

2014· article· en· W2323090503 on OpenAlexvenueno aff
Mélanie Levasseur, Mélanie Couture

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2014
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoping (psychology)Psychological interventionPsychologyDistancingAvoidance copingSocial supportQuality of life (healthcare)Clinical psychologyGerontologySocial psychologyMedicineCoronavirus disease 2019 (COVID-19)PsychiatryDiseasePsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: It is important to understand the coping strategies that optimize or restrict participation or quality of life, especially for older adults. PURPOSE: The purpose of this study was to examine the associations between, on the one hand, problem- and emotion-focused coping strategies used to deal with aging limitations or health problems and, on the other hand, participation and quality of life. METHOD: A cross-sectional design was used with 82 community-dwelling participants aged 65 and older. FINDINGS: Participants used both problem-focused (distancing, self-control) and emotion-focused (seeking social support, planful problem solving, positive appraisal) coping strategies to deal with aging limitations or health problems. Only a few moderately significant associations were found except for escape-avoidance coping strategies, which were significantly associated with lower participation and quality of life. IMPLICATIONS: Before developing interventions to improve or maintain older adults' participation and quality of life, more studies are needed to better understand coping strategies used by older adults to deal with aging limitations or health problems and especially escape-avoidance strategies.

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)0.001
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 score0.054
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.211
GPT teacher head0.460
Teacher spread0.249 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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