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Record W2087012777 · doi:10.1002/casp.742

Narrating the negative consequences of elder care and familial obligation in Atlantic Canada

2003· article· en· W2087012777 on OpenAlexaboutno aff
Darrin Hodgetts, Daryl Pullman, Atsushi Goto

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsObligationInterpersonal communicationPsychologyFocus groupSocial psychologyHealth careSociologyGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract At a time when renewed emphasis is being placed on the role of the family in providing care for elderly people, shifts in family structures and the demographic profile of societies such as Atlantic Canada are impacting on community structures foundational to familial caregiving practices. Research shows that family members who attempt to provide familial care without adequate support risk negative health and interpersonal consequences. Hence many find it necessary to reflect upon their familial obligations. This article investigates the storytelling processes through which caregivers, aged between 45 and 55 years ( N = 26), make sense of their efforts to provide care. We explore the ways in which participants in four focus groups and 12 interviews refer to negative consequences of caring in order to navigate dilemmas arising from their enactment of familial obligations in the absence of adequate support. Copyright © 2003 John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.689

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.007
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.328
Teacher spread0.303 · 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 designQualitative
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

Citations8
Published2003
Admission routes1
Has abstractyes

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