Bibliographic record
Abstract
OBJECTIVE: To discuss what constitutes elder abuse, why family physicians should be aware of it, what signs and symptoms might suggest mistreatment of older adults, how the Elder Abuse Suspicion Index might help in identification of abuse, and what options exist for responding to suspicions of abuse. SOURCES OF INFORMATION: MEDLINE, PsycINFO, and Social Work Abstracts were searched for publications in English or French, from 1970 to 2011, using the terms elder abuse, elder neglect, elder mistreatment, seniors, older adults, violence, identification, detection tools, and signs and symptoms. Relevant publications were reviewed. MAIN MESSAGE: Elder abuse is an important cause of morbidity and mortality in older adults. While family physicians are well placed to identify mistreatment of seniors, their actual rates of reporting abuse are lower than those in other professions. This might be improved by an understanding of the range of acts that constitute elder abuse and what signs and symptoms seen in the office might suggest abuse. Detection might be enhanced by use of a short validated tool, such as the Elder Abuse Suspicion Index. CONCLUSION: Family physicians can play a larger role in identifying possible elder abuse. Once suspicion of abuse is raised, most communities have social service or law enforcement providers available to do additional assessments and interventions.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".