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Record W3000493215 · doi:10.1017/s0144686x19001806

Trajectories of family care over the lifecourse: evidence from Canada

2020· article· en· W3000493215 on OpenAlexafffundabout
Janet Fast, Norah Keating, Jacquie Eales, Choong Kim, Yeonjung Lee

Bibliographic record

VenueAgeing and Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersEconomic and Social Research CouncilAGE-WELL
KeywordsContext (archaeology)Diversity (politics)Health careSocial carePsychologySample (material)NursingSociologyMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract In the midst of a ‘care crisis’, attention has turned again to families who are viewed both as untapped care resources and as disappearing ones. Within this apparent policy/demographic impasse, we test empirically theorised trajectories of family care, creating evidence of diverse patterns of care across the lifecourse. The study sample, drawn from a Statistics Canada national survey of family care, comprised all Canadians aged 65 and older who had ever provided care (N = 3,299). Latent Profile Analysis yielded five distinct care trajectories: compressed generational, broad generational, intensive parent care, career care and serial care. They differed in age of first care experience, number of care episodes, total years of care and amount of overlap among episodes. Trajectories generally corresponded to previously hypothesised patterns but with additional characteristics that added to our understanding of diversity in lifecourse patterns of care. The five trajectories identified provide the basis for further understanding how time and events unfold in various ways across lifecourses of care. A gap remains in understanding how relationships with family and social network members evolve in the context of care. A challenge is presented to policy makers to temper a ‘families by stealth’ policy approach with one that supports family carers who are integral to health and social care systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.265
Teacher spread0.245 · 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 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

Citations45
Published2020
Admission routes3
Has abstractyes

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