Focusing on Caregiver Neglect: A Novel Strategy for Mistreatment of Older Adults Screening and Intervention
Bibliographic record
Abstract
Mistreatment of older adults is common and has serious health consequences but is underrecognized and underreported. Screening for mistreatment of older adults and initiation of intervention in primary care clinics may be helpful, but the value of existing tools is not supported by evidence. We argue that shifting the focus to individual subtypes of mistreatment of older adults can provide improved approaches to screening and ultimately to intervention. We focus on the example of caregiver neglect, the subtype associated with highest mortality. To develop caregiver neglect screening and intervention programs and to measure their effectiveness and impact, we assert that it is critical to: (1) define the phenomenon, (2) develop a conceptual model to explain why it occurs, (3) develop measurement strategies, and (4) systematically examine existing literature. We describe here the initial components of this development process.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".