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Record W2895222390 · doi:10.1017/s0144686x18001125

‘Add info and stir’: an institutional ethnographic scoping review of family care-givers’ information work

2018· article· en· W2895222390 on OpenAlexaff
Nicole Dalmer

Bibliographic record

VenueAgeing and Society · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsWestern University
Fundersnot available
KeywordsWork (physics)EthnographySociologyCare workDementiaNursingPublic relationsPsychologyGerontologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract Family care-givers are increasingly expected to find, understand and use information to meet the complex needs of older adults in their care. A significant number of studies, however, continue to report that care-givers’ information needs are unmet. Following Arksey and O'Malley's scoping review framework, I examined 72 articles for the range and extent of available research on the information work done by family care-givers of community-dwelling older adults living with dementia. To untangle the complex relationship between information and care, this scoping review maps out (a) the ways scholarly literature conceptualises the informational components of family care-givers’ work and (b) the degree to which scholarly research acknowledges these components as work. An institutional ethnography inflection enhanced the scoping review framework, enabling the privileging of lived experiences, questioning of assumptions of language used, attending to authors’ positioning and highlighting care-givers’ information work made invisible throughout the processes of academic research.

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)0.154
metaresearch head score (Gemma)0.232
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: none
Teacher disagreement score0.154
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1540.232
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0250.025
Science and technology studies0.0080.009
Scholarly communication0.0080.011
Open science0.0030.011
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.389
Teacher spread0.342 · 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

Citations39
Published2018
Admission routes1
Has abstractyes

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