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Record W4366709754 · doi:10.47674/9781447366188

Unpaid Work in Nursing Homes

2023· book· en· W4366709754 on OpenAlexfundaboutno aff

Bibliographic record

VenuePolicy Press eBooks · 2023
Typebook
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsWork (physics)Unpaid workPandemicCoronavirus disease 2019 (COVID-19)NursingNursing homesSociologyPaid workCare workMedicineEngineering

Abstract

fetched live from OpenAlex

EPDF and EPUB available Open Access under CC-BY-NC-ND licence. The COVID-19 pandemic has made unpaid care more visible through its absence, while also increasing the need for it. Drawing on a range of research projects covering Canada, Germany, Norway, Sweden, the UK and the US, this book documents a broad spectrum of unpaid work performed by residents, relatives, volunteers and staff in nursing homes. It demonstrates how boundaries between paid and unpaid work are flexible, varying considerably with conditions, time, place and intersectional populations. By examining the complex labour process within nursing homes, this book provides insight and understanding which will be critical in planning for nursing home care post-pandemic.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.091
GPT teacher head0.440
Teacher spread0.349 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes2
Has abstractyes

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