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Record W4383103020 · doi:10.1007/s10834-023-09899-8

Valuing the Contributions of Family Caregivers to the Care Economy

2023· article· en· W4383103020 on OpenAlexafffundabout
Janet Fast, Karen A. Duncan, Norah Keating, Choong Kim

Bibliographic record

VenueJournal of Family and Economic Issues · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of ManitobaUniversity of Alberta
FundersAGE-WELL
KeywordsRealmCare workContext (archaeology)Work (physics)Dominance (genetics)Unpaid workValue (mathematics)ContextualizationDemographic economicsBusinessEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to estimate the monetary value of Canadians’ family care work, to highlight inequalities within the family care sector and place this work within the care economy. Using Statistics Canada’s 2018 General Social Survey, we estimated the replacement cost of the 5.7 billion hours of respondents’ care work at between $97.1 billion and $112.7 billion. We used descriptive, backward stepwise regression and dominance analyses to examine the distribution of care responsibilities among caregivers. Caregivers comprised 22.1% of the sample (6.8 million Canadians). Living arrangement explained most (81-83%) of the variance in the value of unpaid care work, followed by generation (14-15%), income (2%) and gender (1-2%). These findings provide powerful evidence of the economic value of family care work and of the inequalities among family caregivers in the magnitude of their contributions. Monetizing the value of family care makes it more visible, locates it in the context of the broader care economy and establishes its relationship to the much more visible and valued realm of paid care work. This contextualization also responds to global action plans and resolutions urging governments to create systems of long-term and continuing care for people with chronic conditions and disabilities rather than imposing sole responsibility on unpaid caregivers.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.448

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 studies0.0010.000
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 head0.020
GPT teacher head0.308
Teacher spread0.288 · 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 designNot applicable
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

Citations33
Published2023
Admission routes3
Has abstractyes

Explore more

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