Gray, black, and blue: the state of research and intervention for intimate partner abuse among elders
Bibliographic record
Abstract
Though not as common as in younger populations, intimate partner abuse (IPA) among elders is a significant and often overlooked problem. In this article, we focus on problems for research and intervention. We begin with a brief review of the phenomena of elder abuse and IPA, highlighting problems resulting from definitional issues and inconsistencies in research methodology. The balance of the paper comprises a discussion of problems for intervention. Drawing from the IPA and elder abuse literatures, risk factors unique to IPA among elders are presented, and limitations of existing screening and risk assessment instruments for use within this context are identified. The focus then shifts to legal considerations when working with elders who have experienced or perpetrated IPA. Our goals are to synthesize the elder abuse and IPA literatures, identify limitations within both, and to reflect upon the state of knowledge regarding IPA among elders.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".