Psychometric Properties of The Five-Item Victimization of Exploitation (FIVE) Scale: A Measure of Financial Abuse of Older Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Elder mistreatment affects at least 1 in 10 older adults. Financial abuse, or exploitation, of older adults is among the most commonly reported forms of abuse. Few validated measures exist to measure this construct. We aim to present a new psychometrically validated measure of financial abuse of older adults. RESEARCH DESIGN AND METHODS: Classical test theory and item response theory (IRT) methodologies were used to examine a five-item measure of financial abuse of older adults, administered as part of the New York State Elder Mistreatment Survey. RESULTS: Factor analysis revealed a single factor best fits the data, which we labeled as financial abuse. Moreover, IRT analyses revealed that these items discriminated well between abused and nonabused persons and provided information at high levels of the latent trait θ, as is expected in cases of abuse. DISCUSSION AND IMPLICATIONS: The Five-Item Victimization of Exploitation Scale has acceptable psychometric properties and has been used successfully in large-scale survey research. We recommend this measure as an indicator of financial abuse in elder abuse, or mistreatment prevalence research studies.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".