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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".