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Record W2272298078 · doi:10.1080/15017419.2015.1081616

The marital status of disabled women in Canada: a population-based analysis

2015· article· en· W2272298078 on OpenAlexaffabout
Amber Savage, David McConnell

Bibliographic record

VenueScandinavian Journal of Disability Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMarital statusEducational attainmentCohabitationDemographyPopulationPsychologyGerontologySample (material)MedicineGeographySociologyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Surprisingly little population-based data exist on the marriage and relationship patterns of disabled women. This study investigated the formation and dissolution of marriage and common-law relationships involving disabled women in Canada. A secondary data analysis of the 2009 Canadian Community Health Survey was undertaken. The effective sample size for the study was 41,650 women, 18–59 years, including 9450 disabled women. The findings suggest that disabled women in Canada are less likely to be married or in a cohabiting relationship, although it appears that most will marry at some point. Among disabled women, those with early onset conditions, cognitive impairment, mobility limitations and lower levels of educational attainment are more likely to remain single, that is, never having entered into a cohabiting relationship. A plausible explanation for the observed differences in marital status is that disabled women have less opportunity to meet potential partners and form lasting cohabiting relationships.

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 score0.016
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.008
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.374
Teacher spread0.325 · 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

Citations55
Published2015
Admission routes2
Has abstractyes

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