Intervention mechanisms of an experience sampling intervention for spousal carers of people with dementia: a secondary analysis using momentary data
Bibliographic record
Abstract
Objectives: A psychosocial intervention for spousal carers of people with dementia promoted emotional well-being through self-monitoring and personalized feedback, as demonstrated in a previous randomized controlled trial. The mechanism behind the intervention effects is thought to lie in increased awareness of, and thus, engagement in behaviours that elicit positive emotions (PA). This secondary analysis tests the assumption by investigating momentary data on activities, affect, and stress and explores the relevance of personalized feedback compared to self-monitoring only.Methods: The intervention was based on the experience sampling method (ESM), meaning that carers self-monitored own affect and behaviours 10 times/day over 6 weeks. The experimental group received personalized feedback on behaviours that elicit PA, while the pseudo-experimental group performed self-monitoring only. A control group was also included. ESM-data of 72 carers was analysed using multilevel mixed-effects models.Results: The experimental group reported significant increases in passive relaxation activities over the 6 weeks (B = 0.28, SE = 0.12, Z = 2.43, p < .05). Passive relaxation in this group was negatively associated with negative affect (r = –0.50, p = .01) and positively associated with activity-related stress (r = 0.52, p = .007) from baseline to post-intervention. Other activities in this or the other groups did not change significantly.Conclusion: Carer’s daily behaviours were only affected when self-monitoring was combined with personalized feedback. Changing one’s daily behaviour while caring for a person with dementia is challenging and aligned with mixed emotions. Acknowledging simultaneously positive and negative emotions, and feelings of stress is suggested to embrace the complexity of carer’s life and provide sustainable support.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".