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Record W3110192082 · doi:10.1177/0733464820976436

Who Cares? Preferences for Formal and Informal Care Among Older Adults in Québec

2020· article· en· W3110192082 on OpenAlexafffundabout
Kyuho Lee, Marina Revelli, Daniel Dickson, Patrik Marier

Bibliographic record

VenueJournal of Applied Gerontology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGerontologyPsychologyNursingMedicine

Abstract

fetched live from OpenAlex

Policy makers, practitioners, and scholars are increasingly examining the types of care services (formal vs. informal) offered to older adults. This study evaluates predictors of these adults' preferences for care types in Québec, Canada, based on a province-wide survey inserted in a magazine of the largest seniors' club in Canada (FADOQ). More than twice as many respondents indicated a preference for formal rather than informal care. Multinomial logistic regressions demonstrate that older adults' past and current experiences and perceptions of formal and informal services continue to play an important role in their preference formation regarding care services. The study determined that preferring informal care is significantly more prevalent when one is accustomed to this type of care, and that men are significantly more likely to prefer informal care than women, and that lower-income individuals are less likely to favor formal care.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.345
Teacher spread0.311 · 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 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

Citations16
Published2020
Admission routes3
Has abstractyes

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