An Investigative Tool for Detecting Elder Abuse
Bibliographic record
Abstract
Elder abuse is estimated to affect one in ten individuals 60 years of age and older and has been significantly associated with an increased risk of mortality. However, no clear data exist on the number of deaths that result from elder abuse or neglect. The potential contribution of abuse and neglect to the death of an elder is rarely investigated, as natural deaths are expected with advancing age. Elders are often reliant on others for care making them a vulnerable population. Although the deaths of other vulnerable populations, including children, are routinely investigated, no protocols for elder death investigation have been enacted. We propose the implementation of an investigative tool to assess the elder decedent and residence for indicators of abuse or neglect. Investigations may assist in differentiating self-neglect and caretaker neglect. Decedent observations include: evidence of injuries, personal hygiene, malnutrition and/or dehydration, decubitus ulcers, evidence of restraint, unexplained vaginal or anal bleeding, and previous reports with Adult Protective Services. To differentiate between self-neglect and caretaker neglect, an assessment of the level of dependence on others for activities of daily living and the level of involvement of the caretaker are determined. The decedent's living condition assessment includes evidence of forced isolation; lack of food, water or utilities; soiled clothing and/or bedding; filthy or unsafe living conditions; and inappropriate administration of medications. With proper training, medical examiners can easily implement these protocols. Such information is extremely valuable for determining whether further investigation and examination of the decedent is warranted.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".