USE OF MOTIVATIONAL INTERVIEWING TO INTERVENE ELDER ABUSE: A RANDOMIZED CONTROL TRIAL
Bibliographic record
Abstract
Abstract Elder abuse causes serious harm to individuals, families, and society. There is an urgent need to develop and test appropriate interventions to tackle it. This study tested the effectiveness of a motivational interviewing (MI)-based intervention in reducing elder abuse severity and promoting positive changes among community dwelling older Chinese in Hong Kong. The intervention involved a 90-minute individual engagement session and three 30-minute booster sessions delivered by trained MI facilitators. Personal goals for improvement of known risk factors such as poor physical and psychological health, lack of social support were set out followed by the application of MI processes. Pre- and post-test comparison of 33 intervention cases and 28 control cases are presented here. Analysis of pre-treatment differences found significant difference between the two groups in their monthly income, number of offspring and co-residing persons, physical frailty, general self-efficacy and abuse severity. Difference in differences technique was applied to mitigate the effects of extraneous factors and selection bias. Results show that intervention group performed significantly better than the control group upon completing the intervention program. Significant between group differences were found in terms of changes in psychological distress (GHQ, z=-3.043, p< 0.05), general self-efficacy (GSES, z=-4.988, p< 0.05), social support (MSPSS, z=-3.080, p< 0.05), abuse severity (CTS2, z=-2.593, p< 0.05), and perceived ability to change (z=-3.749, p< 0.05). MI has demonstrated good potential for elder abuse intervention. Further study should be conducted to identify key elements in MI leading to positive changes and refine the model for elder abuse prevention and 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".