Web-Based Intervention Using Self-Compassionate Writing to Induce Positive Mood in Family Caregivers of Older Adults: Quantitative Study
Bibliographic record
Abstract
BACKGROUND: Caregiver burden can impact the mental health of family caregivers, but self-compassion may help reduce this impact. Brief self-compassion interventions have been shown to be useful but have not been tested in family caregivers of older adults. OBJECTIVE: This study aimed to test the effects of a brief self-compassion intervention and its components (self-kindness, common humanity, and mindfulness) on mental well-being and mood when reflecting on difficult family caregiving experiences. METHODS: British caregivers were recruited through a web-based panel. Three experimental studies manipulated the self-compassion intervention. In study 1 (n=206) and study 2 (n=224), participants wrote about a difficult caregiving experience while focusing on 1 self-compassion component (self-kindness, common humanity, or mindfulness). In study 3 (n=222) participants focused on all components. Self-compassion, serenity, guilt, and sadness were measured. RESULTS: In studies 1 and 2, condition effects showed mindfulness unexpectedly lowered mood. Inconsistent and modest benefits to affect were achieved by engagement in self-kindness and common humanity in study 1 (guilt [lowered]: P=.02 and sadness [lowered]: P=.04; serenity [nonsignificantly raised]: P=.20) and also in study 2 (sadness [nonsignificantly lowered]: P=.23 and guilt [nonsignificantly lowered]: P=.26; serenity [raised]: P=.33); significant benefits for self-compassion and mood were found in study 3 (serenity [raised]: P=.01, kindness [raised]: P=.003, and common humanity [raised]: P≤.001; guilt [lowered]: P<.001 and sadness [lowered]: P≤.001). More intensive efforts should be made to promote self-compassion in caregivers of older adults, with caution advised when relying primarily on mindfulness approaches. CONCLUSIONS: Self-compassionate writing may be beneficial for family caregivers, but more intensive interventions are needed. Further research is needed to determine the optimal dosage and content for achieving the greatest effects.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".