SELF-NEGLECT CO-OCCURS WITH AND IS A RISK FACTOR FOR ELDER MISTREATMENT
Bibliographic record
Abstract
Abstract Elder mistreatment is abuse, neglect, or exploitation occurring in a relationship involving an expectation of trust.1 Yearly, 10%–14% of community dwelling older adults experience mistreatment, with only 15% of cases being reported to Adult Protective Services (APS) for investigation.2 Self-neglect (not defined as elder mistreat- ment) comprises 50% of APS investigations and 65% of substantiated APS cases.3 How self-neglect relates to other forms of mistreatment is not well understood. Maine APS data was analyzed to delineate the relationship between self-neglect and elder mistreatment. Self-neglect as a risk factor for elder mistreatment has important implications for policy, practice, and the prevention of future harms. Our analysis demonstrates that Self-neglect frequently co-occurs with elder mistreatment. A first allegation of self-neglect, substantiated or not, is associated with a shorter time to elder mistreatment and an estimated 33% of all cases of elder mistreatment could be attributed to self-neglect. These findings have important implications for prevention, policy, and practice. While causality cannot be presumed—a limitation in all retrospective analyses—elder mistreatment and self-neglect share risk factors: cognitive impairment, physical disability, lack of social support, and social isolation. Our analysis suggests that early intervention, even in unsubstantiated allegations of self-neglect, may prevent subsequent mistreatment.
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 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 | 0.012 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".