Understanding burden differences between men and women caregivers: the contribution of care-recipient problem behaviors
Bibliographic record
Abstract
BACKGROUND: This study was carried out to determine why women caring for men report more burden than other caregivers, and to further examine the role of care-recipient problem behaviors as determinants of burden. METHOD: A sample of 557 primary caregivers of community-dwelling individuals referred to a memory clinic was used. All care-recipients had a diagnosis of Alzheimer's disease (NINCDS-ADRDA). Data on care-recipient function, caregiver attributes, external supports and caregiver burden were obtained on the first visit. Hierarchical regression models were used to determine the contribution of gender, after controlling for care-recipient status, caregiver attributes, and external supports. RESULTS: This model explained 46% of the variability in caregiver "role burden", with care-recipient problem behaviors and dependence in instrumental activities of daily living. The caregiver/care-recipient gender interaction explained an additional 4% of the variance (p = 0.001); women caring for men scored 5.61 higher on the burden scale than other caregivers. Specific problem behaviors (e.g., anger) were more problematic for women caregivers than men. CONCLUSION: These results indicate that the experience of men and women caregivers may be different despite seemingly identical circumstances, and highlight the need for interventions geared to the specific needs of women caregivers.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".