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Record W2177373446 · doi:10.4236/aar.2015.46023

Identity Processes, Depression, and the Aging Self<br/>—A Norwegian Study

2015· article· en· W2177373446 on OpenAlexaff
Gail Low, Mary Kalfoss, Liv Halvorsrud

Bibliographic record

VenueAdvances in Aging Research · 2015
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Alberta
FundersEuropean Commission
KeywordsStructural equation modelingNorwegianPsychosocialPsychologyAttributionSuccessful agingClinical psychologyFeelingHuman physical appearanceDevelopmental psychologyGerontologyPsychiatrySocial psychologyMedicine

Abstract

fetched live from OpenAlex

The Identity Process Theory is a theory of how people adapt to aging. According to this theory, older people attribute their physical functioning to a more variable state of health or to their age. Health attributions per se help older people avoid negative thoughts and feelings about themselves and their own process of aging. We explored health versus age attributions, and their effects upon depressive symptoms and negativity toward aging among 359 older Norwegians (age range = 60 - 91 years of age). Aging pertained to psychosocial loss, physical change, and psychological growth. Data collected in the 2004 WHOQOL-OLD Norwegian Field Study were analyzed by using a MANOVA and a validatory path analysis. Our findings consistently supported health attributions in relation to psychosocial loss (X2 = 20.37, df = 10, p = 0.03; GFI = 0.98, AGFI = 0.95, RMSEA = 0.05), physical change (X2 = 35.03, df = 14, p = 0.000; GFI = 0.97, AGFI = 0.94, RMSEA = 0.06), and psychological growth (X2 = 22.22, df = 13, p = 0.05; GFI = 0.98, AGFI = 0.96, RMSEA = 0.04). Health attributions increased participants’ depressive symptoms and negativity toward aging, especially toward psychosocial loss (β= - 0.45, p = 0.000) and physical change (β= - 0.48, p = 0.000). We relate these theoretically contradictory findings to Norwegian cultural beliefs and values. We make recommendations for research, including normalizing depressive symptoms and cross-cultural investigations.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.119
GPT teacher head0.502
Teacher spread0.383 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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