A Systematic Evaluation of a Multidisciplinary Social Work–Lawyer Elder Mistreatment Intervention Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study introduces a conceptually based, systematic evaluation process employing multivariate techniques to evaluate a multidisciplinary social work-lawyer intervention model (JASA-LEAP). Logistic regression analyses were used with a random sample of case records (n = 250) from three intervention sites. Client retention, program fidelity, and exposure to multidisciplinary services were significantly related to reduction in mistreatment risk at case closure. Female gender, married status, and living with perpetrator significantly predicted unfavorable outcomes. This study extends the elder mistreatment program evaluation literature beyond descriptive/bivariate evaluation strategies. Findings suggest that a multidisciplinary social work-lawyer elder mistreatment intervention model is a successful approach.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it