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Record W4221080197 · doi:10.1017/s0047279422000022

Does the family care best? Ideals of care in a familialistic care regime

2022· article· en· W4221080197 on OpenAlexaboutno aff
Selma Kadi, Ricardo Rodrigues, Rahel Kahlert, SILVIA HOFMANN, Gudrun Bauer

Bibliographic record

VenueJournal of Social Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
FundersAustrian Science Fund
KeywordsQuarter (Canadian coin)Face (sociological concept)Child careNursingPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

Abstract Informal caregiving for older family members is increasingly recognized and encouraged by public policies. This is even more the case in familialistic countries such as Austria. This article aims to examine the role of the family in the care culture of a familialistic care regime through the experiences of care dyads combining upwards intergenerational family care and formal care services. The analysis is based on framework analysis of 48 face-to-face interviews, almost all conducted separately, with 24 care dyads of older care users (average age 86 years old with moderate to high care needs) and their main informal carers in Austria combining formal and informal care. A quarter of caregivers and care receivers were male. We identified four different ideals (family care, preferences, involvement, gendered care) which were distributed across different groups of Socio-Economic Status (SES) and described by women and men.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.018
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.002
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 head0.012
GPT teacher head0.321
Teacher spread0.309 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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