Does Embeddedness Protect? Personal Network Density and Vulnerability to Mistreatment Among Older American Adults
Bibliographic record
Abstract
OBJECTIVES: This study considers the association between personal network density and risk of elder mistreatment among American adults. METHOD: Using egocentric network data from the National Social Life, Health, and Aging Project, we employ logistic and negative binomial regression to predict recent experience of elder mistreatment. We further unpack the density mistreatment association by linking perpetrators to the victim's network and by assessing their position within its structure. RESULTS: As hypothesized, older adults with dense networks had a lower risk of elder mistreatment. Interestingly, the perpetrators of these harmful acts were often found within seniors' close networks-though there was little evidence to suggest that perpetrators themselves were poorly embedded in the network. DISCUSSION: Results highlight how network-level phenomena can operate distinctively from dyadic mistreatment processes. Dense personal networks seem to provide structural protection against elder mistreatment, even as many offensive acts are committed by those that are close to the victim and relatively well embedded in their network.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".