Bibliographic record
Abstract
The ever-expanding growth of the geriatric population increases the likelihood of abuse and neglect both in the home and in caregiving institutions. In the United States alone, hundreds of thousands of elders are maltreated each year. Only recently has there been a clear public and governmental awareness of elder abuse in all its forms. Elder maltreatment, including abuse and neglect, comprises an act or omission resulting in morbidity and/or mortality of older persons. Six recognized categories of elder maltreatment include physical, sexual, and psychological abuse; financial exploitation; neglect, and a miscellaneous classification that often includes the violation of the elder’s rights. A strong familial relationship between the abused and the abuser exists. In more than two-thirds of cases, an adult child or spouse is the perpetrator. Domestic violence in the family is also a common underlying factor. Recognition of this phenomenon is the initial step in reaching a correct diagnosis. Despite efforts to educate health care providers of elder maltreatment, physicians in the United States have reported only 2% of all abuse cases in recent years. Achieving full recognition of elder maltreatment requires a multidisciplinary effort from many fields, including clinicians, social workers, medicolegal death investigators, and law enforcement to establish comprehensive research and governmental funding. The ultimate goal of such an agenda is to define evidence-based markers for accurate diagnostic methods, which differentiate causes of injury and death by abuse and neglect from those related to normal aging and senescence. In addition to the painstaking scrutiny of the putative victim’s medical and psychological background, circumstantial and scene findings that may suggest elder maltreatment, in any form, must be meticulously investigated.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".