Receiving while giving: The differential roles of receiving help and satisfaction with help on caregiver rewards among spouses and adult‐children
Bibliographic record
Abstract
OBJECTIVE: There is a growing body of literature on the rewards associated with caregiving and the utility of these rewards on buffering the negative consequences of caring for a family member with Alzheimer's disease. Many psychoeducational interventions aim to empower caregivers to seek and obtain help from their social support network, with the expectation that help will enable them to cope more effectively. METHODS: This study investigated the impact of changes in help and changes in satisfaction with help on positive aspects of caregiving for both spouse (N = 254) and adult-child (N = 208) caregivers who attended a psychoeducational intervention. RESULTS: Analyses using structural equation modeling revealed that increases in amount of help and satisfaction with help were significantly linked with increases in caregiver rewards for adult-children. However, only increases in satisfaction with help were significantly related to increases in caregiver rewards for spouses. CONCLUSIONS: These group differences suggest that the quality of support is critical for spouses, whereas both quality and receiving extra help are useful for adult-child caregivers. These findings are discussed in terms of the importance of understanding the differential needs of spouse and adult-child caregivers in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".