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Record W2137030805 · doi:10.1177/0265407510386135

Marital idealization as an enduring buffer to distress among spouses of persons with Alzheimer disease

2010· article· en· W2137030805 on OpenAlexafffund
Norm O’Rourke, Amy Claxton, Anthony L. Kupferschmidt, JuliAnna Z. Smith, B. Lynn Beattie

Bibliographic record

VenueJournal of Social and Personal Relationships · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of British Columbia HospitalAlzheimer Society of CanadaSimon Fraser University
FundersUniversity of British Columbia
KeywordsSpouseIdealizationPsychologyDistressDiseaseAffect (linguistics)Developmental psychologyRemarriageClinical psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Few disease processes affect the dynamics of marital relationships like neurodegenerative disorders. Illnesses such as Alzheimer disease strip older adults of a lifetime of memories and, in the latter stages, even the ability to recognize one’s spouse and children. In cross-sectional research, marital idealization (or the propensity to idealize one’s spouse and relationship) has emerged as significantly associated with the absence of distress among those caring for a spouse with Alzheimer disease. To extend prior findings, multilevel models were computed for the current study to demonstrate that marital idealization predicts both life satisfaction and the relative absence of caregiver burden one year later; moreover, change in marital idealization reflects a corresponding change in the psychological well-being of spouses over this same period ( N = 90). Results of this study are discussed relative to the distinct demands of caring for a spouse with a dementing disorder, the health benefits of positive illusions, and demographic trends suggesting that family caregiving will become increasingly prevalent in coming years.

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 score0.241
Threshold uncertainty score0.591

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 studies0.0010.000
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 head0.029
GPT teacher head0.300
Teacher spread0.270 · 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

Citations14
Published2010
Admission routes2
Has abstractyes

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