Investigation of the Effect of an Online Supportive Education Program on the Family Caregivers’ Resilience and Abuse of People with Dementia: A Controlled Randomized Trial
Bibliographic record
Abstract
Dementia and its progressive nature necessitate care and impose dependency on family caregivers, exposing them to psychological distress and even abuse of people with dementia. The effects of psychological distress and subsequent abuse can be lessened with increased resilience. This study investigates the effect of a supportive online education program on family caregivers’ resilience and abuse of people with dementia. In total, 74 family caregivers of dementia patients participated in this controlled randomized trial, which was conducted from February 2022 to July 2022. Data were gathered using the Walsh Family Resilience Questionnaire, and the Caregiver Abuse Screen. The collected data were analyzed in SPSS software using descriptive and inferential statistics. There was no significant difference between the two groups in terms of baseline variables, resilience, and abuse. The intervention resulted in no significant difference in the scores of resilience ( p = .08) and abuse ( p = .447). Both the intervention ( p = .430) and control ( p = .082) groups showed no evidence of a significant change in resilience between before and after the intervention. However, a significant difference was found in the abuse scores before and after the study in the intervention group ( p = .022). The intervention did not significantly affect family caregivers’ resilience and abuse of people with dementia, but it reduced abuse in the intervention group. Taking note of these results, this study proposes other solutions to improve outcomes, and also education and support of family caregivers to diminish their abuse of people with dementia.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".