MétaCan
Menu
Back to cohort
Record W2113019544 · doi:10.1177/0164027508319655

Effects of Neighborhood and Individual Change on the Personal Outcomes of Recent Movers to Low-Income Senior Housing

2008· article· en· W2113019544 on OpenAlexaffabout
Geoffrey C. Smith, Gina Sylvestre

Bibliographic record

VenueResearch on Aging · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsApartmentExplanatory powerOrdinary least squaresLogistic regressionPsychologySample (material)VariablesRegression analysisDemographic economicsGerontologySocial psychologyEconomicsEconometricsMedicineStatisticsPolitical scienceMathematics

Abstract

fetched live from OpenAlex

The objective of this study was to determine the effects of neighborhood and individual change on the personal outcomes of recent movers to Canadian government-subsidized senior citizen apartment buildings (SCAs). The authors' sample included 137 recent movers to 25 SCA projects in Winnipeg, Manitoba, who participated in a longitudinal survey. The analysis involved testing four logistic and ordinary least squares regression models, with personal state outcomes of the moves (self-rated health, morale, depression, self-esteem) treated as dependent variables. Although the overall performance of the models was moderate, the entry of a block of independent change variables into the regression equations consistently registered statistically significant increases in their explanatory power . Significant predictors of the outcomes included changes in personal resources, everyday travel, and resident appraisals of service, social, and physical components of neighborhood content. The findings suggested that the older person's subjective interpretations of a new residential setting assumed more importance in producing outcomes than objective measures of that settings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.155
GPT teacher head0.402
Teacher spread0.247 · 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

Citations9
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueResearch on AgingSame topicMigration, Aging, and Tourism StudiesFrench-language works237,207