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Record W3126168735 · doi:10.1177/0950017020980985

Relationship-Based Care Work, Austerity and Aged Care

2021· article· en· W3126168735 on OpenAlexaff
Donna Baines, Annabel Dulhunty, Sara Charlesworth

Bibliographic record

VenueWork Employment and Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of British Columbia
FundersAustralian Research Council
KeywordsAusterityManagerialismTypologyPopularityEthnographyContext (archaeology)SacrificeCare workWork (physics)GlobeSociologyPolitical sciencePublic relationsPsychologySocial psychologyPoliticsLawGeography

Abstract

fetched live from OpenAlex

Home care and aged care in English-speaking countries around the globe have enthusiastically taken up a model of work known as ‘relationship-based care’ (RBC). Part of the popularity of RBC is because it does not challenge austerity, underfunding, and extensive managerialism. Instead it works within and through them to foster caring connections between patients, staff, and families, and is able to do so because workers are willing to self-sacrifice for clients. Drawing on case study data collected using a ‘rapid ethnography’ methodology in two large Australian aged care organisations, this article explores workers’ experience of work and contributes to Bolton’s typology of emotion management in the relationship-based care endeavour. Our typology includes: (1) austerity-linked sacrifice; (2) official discourse; (3) faux control; and (4) compulsory time philanthropy. The article contributes to debates on care work, relationship-based care, emotional labour, and emotion management and working in the context of austerity and managerialism.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.031
Scholarly communication0.0080.005
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.325
Teacher spread0.287 · 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

Citations29
Published2021
Admission routes1
Has abstractyes

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