Positive and negative caregiver appraisal and caregiver health outcomes / by Sarah A. Vernon-Scott.
Bibliographic record
Abstract
"The current study examined the role that positive appraisal (i.e., gain) and negative appraisal (i.e., burden) of caregiving can play in understanding caregivers' physical and mental health outcomes. Gender and kinship were examined to investigate any differences in caregiving appraisals or health outcomes. Secondary analyses of two databases, Resources for Enhancing Alzheimer Caregiver Health (REACH) and the Canadian Study of Health and Aging (CSHA) were conducted. Positive appraisal and negative appraisal were established as separate constructs that both change over time. These findings were contributed to the understanding of this newer variable. Adding positive appraisal at the last step of a hierarchical regression after demographics and negative appraisal improved the prediction of caregivers' anxiety, but not other health outcomes (i.e., self-rated health, number of illnesses diagnosed, and depression). Negative social interactions emerged as a significant predictor of health outcomes. Surprisingly, some robust findings from the literature were not replicated. In both databases, women did not report more burden than men. Women caring for men (as opposed to other combinations of caregivers and care recipients) did not report significantly more caregiver burden."--from Abstract
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".