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Record W2966347419 · doi:10.1177/0091415019864603

Canadians Who Care: Social Networks and Informal Caregiving Among Lesbian, Gay, and Bisexual Older Adults in the Canadian Longitudinal Study on Aging

2019· article· en· W2966347419 on OpenAlexafffundabout
Mariam R. Ismail, Nicole G. Hammond, Kimberley Wilson, Arne Stinchcombe

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2019
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsBrock UniversityUniversity of GuelphUniversity of OttawaSaint Paul University
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsLesbianLongitudinal studyGerontologyPsychologyLongitudinal dataPopulationHealth careSocial supportMedicineDemographySociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Canada is experiencing population aging and evidence on the provision of care is based on data collected from majority populations. This analysis compared social networks and patterns of care provision between heterosexual and lesbian, gay, and bisexual (LGB) Canadians between the age of 45 and 85 years. Data were drawn from the Canadian Longitudinal Study on Aging (CLSA), a large national study of health and aging. The results from analysis of baseline data showed that LGB participants were less likely to have children and reported seeing their friends more recently than heterosexual participants. Gay and bisexual men were more likely to provide care support in comparison to heterosexual men. LGB participants were more likely to provide care to friends. The results highlight the importance of considering distinct social networks in the development of policy and practice approaches to support a diverse aging population.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.552

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 head0.043
GPT teacher head0.356
Teacher spread0.312 · 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

Citations15
Published2019
Admission routes3
Has abstractyes

Explore more

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