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Neighborhood Disadvantage and Well‐Being

2014· other· en· W1483450834 on OpenAlexafffund
Richard M. Carpiano

Bibliographic record

VenueThe Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society · 2014
Typeother
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Minority Health and Health DisparitiesMichael Smith Health Research BC
KeywordsDisadvantagedDisadvantageHealth equityPsychological interventionScholarshipSocial determinants of healthQualitative researchEnvironmental healthPsychologySociologyPublic healthMedicinePolitical scienceSocial scienceNursing

Abstract

fetched live from OpenAlex

Abstract Significant attention in recent years has been paid to understanding how the places that people inhabit in their daily lives also matter for their health and well‐being. This entry considers the role of neighborhoods and local area living conditions – particularly socioeconomically disadvantaged locations – for shaping personal health risk factors, health behaviors, and overall health and well‐being. It begins by discussing the various features and mechanisms through which neighborhoods can be health promoting and/or health damaging for residents. Next, it reviews quantitative and qualitative research methods used for assessing the health implications of neighborhood social and physical environments. Finally, it highlights some key emerging areas of scholarship on neighborhood health effects – research foci that aim to fill important knowledge gaps not only about how places matter for health, but also how to design place‐based interventions to promote health and reduce health disparities.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.009
GPT teacher head0.289
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2014
Admission routes2
Has abstractyes

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