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Record W1992501722 · doi:10.1017/s0144686x14000269

Problematising care burden research

2014· article· en· W1992501722 on OpenAlexaff
Mary Ellen Purkis, Christine Ceci

Bibliographic record

VenueAgeing and Society · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of AlbertaUniversity of Victoria
Fundersnot available
KeywordsDyadIntervention (counseling)Materiality (auditing)Psychological interventionPsychologySociologyPublic relationsNursingMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT In this paper we use Alvesson and Sandberg's strategy of problematisation to analyse the assumptions embedded in the development and use of the concept of ‘care-giver burden’. We do this in order to develop an explanation as to why decades of research into the experience of providing home-based care to a family member with dementia has had little effect in relieving or reducing the ‘burden’ of that care. Though some part of this is undoubtedly political, our analysis suggests that key assumptions of the research limit both knowledge development and intervention effectiveness. Especially problematic are first, an overriding focus on the isolated care-giver–recipient dyad as the appropriate object of inquiry and target of intervention, and second, an absence of an analysis of the materiality of care and care-giving practices. The heterogeneity of care situations, including interrelations among people, technologies, objects, spaces and other organisational worlds, appear in much of the research primarily as methodological problems, variables to be subdued through a more rigorous application of method. The high volume of research and acknowledged low impact of interventions, however, suggests that rethinking the nature of care practices, and how we come to know about them, is necessary if we are to develop and implement strategies that will contribute to better outcomes for people.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.031
GPT teacher head0.366
Teacher spread0.335 · 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

Citations51
Published2014
Admission routes1
Has abstractyes

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