DEVELOPING A SCALE TO MEASURE NEGLECT SEVERITY: THE HEALTH-RELATED SEVERITY IN ELDER NEGLECT SCALE
Bibliographic record
Abstract
Abstract Caregiver neglect in older persons can vary dramatically in severity, with differential impact on an older adult’s health. Assessing severity is critical for research and clinical practice but has received focus until recently. To address this gap, we developed a scale to describe the health-related severity of elder neglect using an expert consensus method. In development, the experts conceptualized severity as: (1) the level of risk that neglectful behaviors would cause morbidity or mortality and (2) related timeframe. Additionally, the experts recommended that the scale identify risk for future neglect. The scale was designed iteratively, and, after finalization, we assessed face and construct validity. The final scale was found to have validity. It has 5 levels: not present, not present / potential risk, present / mild, present / moderate, present / severe. Each level has a description to guide assessment. For example, present / mild is described as: “caregiving behaviors not optimal, with potential to create morbidity, but low concern for immediate danger,” present / moderate is: “caregiving behaviors with significant potential to create morbidity within the next 4 weeks,” and present / severe is “caregiving behaviors creating immediate danger of morbidity or mortality -- insufficient access to shelter, food, medication – with alternative living situation or ED visit / hospitalization recommended.” The description of not present / potential risk is: “though neglect not currently occurring, factors present that raise concern for future neglect risk.” Assessing neglect severity using this scale may improve understanding of the phenomenon and inform intervention.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".