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Record W2156007315 · doi:10.1177/0192513x03254519

“In Sickness and in Health”

2003· article· en· W2156007315 on OpenAlexaffabout
Zheng Wu, Margaret J. Penning, Michael Pollard, Randy Hart

Bibliographic record

VenueJournal of Family Issues · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of TorontoUniversity of Victoria
Fundersnot available
KeywordsCohabitationMental healthPhysical healthDemographySelection (genetic algorithm)PopulationPsychologyGerontologyMedicineGeographySociologyPsychiatry

Abstract

fetched live from OpenAlex

Using data from the 1994-95 (Canadian) National Population Health Survey (6,494 women, 5,368 men), we investigated the impact of cohabitation on a range of physical and mental health indicators, controlling for self-selection into cohabitation and other relevant factors. Uncontrolled results indicate that the physical and mental health of cohabitors tends to fall between that of the married and the divorced/separated, widowed, and single/never married. However, when other factors are controlled, health differences between cohabitors and the currently married become nonsignificant. Self-selection, into cohabitation and into marriage, initially appears to play a significant role in accounting for variations in health, but with controls added to the models, selection mostly becomes nonsignificant. We concluded that self-selection at most may explain a small proportion of the variation in health but that protection effects are more likely to explain the positive health advantages of marriage and cohabitation.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.052
GPT teacher head0.413
Teacher spread0.362 · 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 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

Citations240
Published2003
Admission routes2
Has abstractyes

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