Informal Caregiver Burden and Potential Consequences
Bibliographic record
Abstract
Providing care to an adult loved one is often described as a burden, but it can also promote flourishing. The present study examined the extent to which state hope and emotion regulation strategies of positive reappraisal and emotional suppression might account for the relation of burden on both burnout and flourishing in a sample of 162 informal caregivers (54.8% women; Mage = 42.92, SDage = 13.22). It was hypothesized that state hope would mediate the relationship between caregiver burden and both outcomes, with state hope being positively related to flourishing and negatively related to burnout. It was also hypothesized that positive reappraisal and emotional suppression would mediate the relationship between caregivers’ burden and the outcomes, with positive reappraisal predicting greater flourishing and emotional suppression predicting greater burnout, as well as less flourishing. Participants were recruited from the Prolific crowd sourcing platform and had been pre-screened as informal and unpaid caregivers of an adult. They completed a battery of questionnaires that were all self-report in nature for the cross-sectional design. Results indicate that only state hope mediated the relationship between caregiver burden and the outcomes of burnout and flourishing. Contrary to what was hypothesized, emotion regulation strategy was not a mechanism in these relationships. State hope could be a potential avenue for intervention for informal caregivers in that it may impact caregiver burden as well as burnout and flourishing.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".