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Record W2084030946 · doi:10.1007/s12160-011-9310-0

Perception of Neighborhood Disorder and Health Service Usage in a Canadian Sample

2011· article· en· W2084030946 on OpenAlexafffundabout
Alexa Martin‐Storey, Caroline E. Temcheff, Paula L. Ruttle, Lisa A. Serbin, Dale M. Stack, Alex E. Schwartzman, Jane E. Ledingham

Bibliographic record

VenueAnnals of Behavioral Medicine · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of OttawaMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPovertySocioeconomic statusPerceptionCensus tractHealth psychologyGerontologyPsychologyEnvironmental healthSample (material)Public healthMedicineNursingPopulationPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Neighborhood environment, both actual and perceived, is associated with health outcomes; however, much of this research has relied on self-reports of these outcomes. PURPOSE: The association between both perception of neighborhood disorder and neighborhood poverty (as measured by postal code socioeconomic status) was examined in the prediction of health service usage. METHOD: Participants in a longitudinal project were contacted in mid-adulthood regarding their perception of neighborhood disorder. Their census tract data and medical records were drawn from government databases. RESULTS: Higher perceived neighborhood disorder was significantly associated with higher levels of total health services usage, lifestyle illnesses, specialist visits, and emergency room visits, even when neighborhood poverty and individual-level variables were controlled for. Neighborhood poverty was only significantly associated with fewer total hospitalizations. CONCLUSIONS: Higher perceived neighborhood disorder was associated with higher rates of health service usage, suggesting further investigation into the mechanisms by which perceptions of the environment influences health outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.036
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

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

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 head0.246
GPT teacher head0.457
Teacher spread0.211 · 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

Citations24
Published2011
Admission routes3
Has abstractyes

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