Coping strategies and quality of life among Thai family carers of community‐dwelling persons living with dementia: A cross‐sectional study
Bibliographic record
Abstract
AIMS: To examine the association between coping strategies and quality of life (QOL) among Thai family carers of persons living with dementia. DESIGN: A descriptive correlational quantitative design. METHODS: Data were collected between January 2021 and April 2021. A multi-pronged approach for recruitment was used. Participants completed measures assessing carer demographic characteristics, coping styles, QOL, perceived stress and perceived social support. Hierarchical multiple regression analysis was used to determine the association between types of coping strategies used and QOL scores, adjusting for carer characteristics and carers' stress and social support. RESULTS: There were 86 participants (mean age 52.84 years), and the majority were female (87.2%). After adjusting for covariates, hierarchical multiple regression revealed that only positive emotion-focused coping demonstrated a statistically significant association with total QOL scores. Problem-focused coping and negative emotion-focused coping were not significantly associated with total QOL scores of Thai family carers of persons living with dementia. CONCLUSION: Positive emotion-focused coping was associated with improved QOL scores. This finding supports the use of positive emotion-focused coping in Thai family carers of persons living with dementia, which potentially could improve the QOL of this population. IMPACT: It is essential to differentiate between positive and negative emotion-focused coping to generate valid estimates of the association between coping and QOL. Nurses should encourage carers to use positive emotion-focused coping strategies as these strategies were found to be helpful in enhancing carers' QOL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".