Motivational Interviewing vs Case Management for Elder Abuse Intervention: A Comparison Study
Bibliographic record
Abstract
Abstract Elder abuse is a serious public health issue. Considerable efforts have been made to develop interventions to address this issue. This study compared the effectiveness of a 4-session motivational interviewing (MI) program and an 8-session case management (CM) program in reducing abuse severity, promoting physical and mental health, general self-efficacy, social support, and motivation to change. Baseline data was collected from 129 older adults screening positive for elder abuse in a community survey. Assessments were repeated at the end of the intervention for the 86 cases receiving MI intervention and 43 cases receiving case management, and at 3-month follow up for 20 cases in the MI group. No demographic difference was observed between cases assigned to MI and CM at baseline. Wilcoxon signed-rank tests were conducted to examine intervention outcomes by different time points. At post-test, the MI group demonstrated significant improvement in abuse severity, psychological distress, general self-efficacy, social support and motivation to change (p<.001). The CM group also showed improvement in these areas with the exception of general self-efficacy (p<.01). Significant difference was observed between MI and CM group in their improvement in general self-efficacy. Abuse severity and psychological distress continued to drop at 3-month post-intervention (p<.05). Abuse severity and psychological distress continued to decrease between post-intervention and 3 month-follow-up (p<.05). Our findings suggest that MI is a cost-effective intervention for reducing abuse severity and risk.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".