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Record W2013744461 · doi:10.1080/15350770801955115

Grandparent Health and Young Adults' Judgments of Their Grandparent-Grandchild Relationships

2008· article· en· W2013744461 on OpenAlexaff
Susan D. Boon, Megan J. Shaw, Stacey L. MacKinnon

Bibliographic record

VenueJournal of Intergenerational Relationships · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Prince Edward IslandUniversity of Calgary
Fundersnot available
KeywordsGrandparentPsychologyGrandchildDevelopmental psychologyPerceptionSocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Using the Common-Sense Model of illness representations (Leventhal, Myer, & Nerenz, 1980 Leventhal, H., Meyer, D. and Nerenz, D. 1980. “The common sense representation of illness danger”. In Contributions to medical psychology, Edited by: Rahcman, S. Vol. 2, 7–30. New York: Pergamon Press. [Google Scholar]) as a framework, we investigated impairment-related variables as predictors of young adults' experiences in relationships with ill/disabled grandparents. Undergraduates (N = 153) completed a questionnaire about their relationships with grandparents who lived with cognitive, physical, or psychological impairment(s). The extent to which participants worried about their grandparents' health/wellbeing and their perceptions of (a) the severity of their grandparents' impairments and (b) the degree to which these impairments affected areas of their own lives were predictors. Satisfaction with contact, investment in the relationship, and family strain resulting from the grandparents' impairment served as criterion variables. Results support the utility of examining grandparent health in research on grandparent-grandchild relationships.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.287
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2008
Admission routes1
Has abstractyes

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