The Association of Self-esteem With Caregiving Demands, Coping, Burden, and Health Among Caregivers of Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND: We investigated how caregiver self-esteem was associated with caregiving demands, coping, burden, and health. OBJECTIVE: The aim of this study was to investigate how caregiver self-esteem is associated with caregiving demands, coping, burden, and health. METHODS: Sixty-one caregivers of breast cancer patients were selected from a study conducted at a cancer clinic in the Southeastern region of the United States. Guided by the revised Stress and Coping Theory, a secondary analysis of cross-sectional data was conducted. We used structural equation modeling to analyze paths between caregiver self-esteem and caregiving demands (ie, hours spent on caregiving), coping, burden, and health. RESULTS: Caregivers who effectively coped with stressful situations through strategies such as positive thinking, seeking social support, and problem solving were more likely to have higher levels of self-esteem; in turn, higher levels of self-esteem decreased caregiver burden and improved caregiver overall health. CONCLUSIONS: This study highlights the importance of self-esteem among caregivers of breast cancer patients. Additional research is needed to provide more insight into the influence of coping strategies on caregiver self-esteem, as well as the role of caregiver self-esteem on caregivers' and patients' well-being. IMPLICATION FOR PRACTICE: Healthcare providers need to consider caregiver self-esteem and other associated caregiver characteristics to identify caregivers at risk of higher perceived levels of burden and poor overall health.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".