NEIGHBORHOOD CONDITIONS AND SELF-NEGLECT IN LATER LIFE: LONGITUDINAL EVIDENCE FROM THE NSHAP
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".