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Record W2983088969 · doi:10.1093/geroni/igz038.1025

NEIGHBORHOOD CONDITIONS AND SELF-NEGLECT IN LATER LIFE: LONGITUDINAL EVIDENCE FROM THE NSHAP

2019· article· en· W2983088969 on OpenAlexaff
Laura Upenieks, James Iveniuk, Markus H. Schafer

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsWellesley InstituteUniversity of Toronto
Fundersnot available
KeywordsNeglectPsychologyCrowdingContext (archaeology)PerceptionDisadvantageDevelopmental psychologyGerontologyPsychiatryMedicineGeographyCognitive psychology

Abstract

fetched live from OpenAlex

Abstract Self-neglect includes persistent inattention to personal hygiene and the conditions of one’s immediate living environment and is known to be associated with an increased risk of mortality among older adults. Although previous studies have shown that many individual factors predict self-neglect, neighborhood characteristics have received much less attention. Extant research has yet to consider connections between the conditions of one’s neighborhood and self care over time. Using nationally representative longitudinal data from the National Social Life, Health, and Aging Project (NSHAP), we consider several features of neighborhood context in later life, including self-reported perceptions of neighborhood cohesion and neighborhood danger, neighborhood disorder (measured by interviewer ratings), and concentrated neighborhood disadvantage (using census data). Adjusting for individual-level factors (including social connection, physical and cognitive health, and demographics), results from both lagged dependent variable and cross-lagged panel models find higher levels of neighborhood disorder to be associated with higher self-neglect scores (measured by interviewer ratings) over time. Social cohesion, perceived neighborhood danger, and collective efficacy were not associated with self-neglect when controlling for neighborhood disorder. These findings suggest that improving neighborhood disorder may be an effective approach for self-neglect prevention in later life

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 score0.014
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.327
Teacher spread0.290 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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