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Record W2139472855 · doi:10.1093/geronb/gbs058

A Cross-National Comparison of Reminiscence Functions Between Canadian and Israeli Older Adults

2012· article· en· W2139472855 on OpenAlexaffabout
Norm O’Rourke, Sara Carmel, Habib Chaudhury, Natalia Polchenko, Yaacov G. Bachner

Bibliographic record

VenueThe Journals of Gerontology Series B · 2012
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReminiscenceBoredomPsychologyRecallDevelopmental psychologySocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: Recently, a model of reminiscence and well-being has emerged in which reminiscence functions have been shown to predict both the mental and physical health of middle-aged and older adults. Yet this model has thus far been verified only with North American, Western European, and Australian participants. This study was undertaken to compare the latent structure of responses between Canadian and Israeli older adults to ascertain if 8 distinct reminiscence functions map onto 3 second-order factors which, in turn, contribute significantly to measurement of an overarching reminiscence latent construct. METHOD: For this study, 336 English Canadian and 206 Jewish Israeli adults more than 49 years of age provided responses for this study via an Internet website constructed specifically for this study. RESULTS: Our findings demonstrate the psychometric equivalence as well as various cross-cultural differences in the relative strength of association between latent constructs (boredom reduction, bitterness revival, identity, and the overall contribution of self-negative functions to overall reminiscence). DISCUSSION: We discuss various historical and geo-political factors that may account for these differences. For instance, recurrent war, ongoing terror, and regional instability make living and aging in Israel distinct from Canada. This model of reminiscence functions would appear sufficiently sensitive to capture cross-national differences.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.416
Teacher spread0.324 · 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.

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

Citations29
Published2012
Admission routes2
Has abstractyes

Explore more

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