THE IMPORTANCE OF COMMUNITY COHESION FOR AFRICAN AMERICAN CAREGIVERS: A MEAN TO COMPENSATE FOR A LACK OF SUPPORT?
Bibliographic record
Abstract
Caregiver burden has been approached mainly at the individual level from the perspective of the patient or the caregiver. This study examines the influence of community on caregivers’ mental health. Data came from 281 spousal caregivers of five waves (2010 - 2014) of the Health and Retirement Study. The result of growth curve model showed that community cohesion was not a significant predictor for the whole group. However, multiple group analysis showed community cohesion predicted lower level of and faster decrease in depressive symptoms for African American caregivers, whereas it was not related to other groups of caregivers. This relationship may be caused in part by the lack of access and support African Americans received from public policies, as evidenced in the literature on race and the welfare state. This, in turn, may accentuate the importance of community cohesion for the well-being of African American caregivers compared to other groups.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".