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Record W3094142095 · doi:10.29173/topo35

Differences in the effect of homeownership on health status across health regions in Alberta, 2011-2012

2017· article· en· W3094142095 on OpenAlexfundvenueaboutno aff
Meryn Severson

Bibliographic record

VenueTopophilia · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsProxy (statistics)Socioeconomic statusAffordable housingDemographic economicsLogistic regressionDebtHealth and Retirement StudyHousing tenureEnvironmental healthHealth equityBusinessSocioeconomicsGeographyEconomic growthGerontologyHealth careEconomicsMedicineFinance

Abstract

fetched live from OpenAlex

The housing we live in - from the type and the location to our homeownership status - impacts our health status. Housing is one of the most central environments individuals live in, and as a socio-economic determinant of health, has disproportionate impact on certain groups. Previous research indicates that homeowners tend to have better health than renters. However, this relationship changes when living in unaffordable housing. Organizations have issued numerous warnings about rising unaffordability, debt, and home prices in Canada. In this paper, I focus on the effects of homeownership status in the five different health regions in Alberta, stratified by housing affordability. Using Statistics Canada’s Canadian Community Health Survey from 2011- 2012 in a logistic multivariate regression, I find that homeownership is positively associated with self-reported good or better health status, but that the association was smaller in less affordable regions. This suggests living in regions that are less affordable dampens the health benefits of homeownership. These findings also support the the idea that homeownership is more than a proxy for socioeconomic status and has its own effects on health.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.095
GPT teacher head0.478
Teacher spread0.383 · 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.

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

Citations1
Published2017
Admission routes3
Has abstractyes

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