Caregiver strain among non-Hispanic Black and Hispanic male caregivers with self-reported chronic health conditions
Bibliographic record
Abstract
Objectives Caregiver strain often stems from unmet needs and is a risk factor for poor physical and psychological health. This study aims to identify factors associated with caregiver strain among middle-aged and older non-Hispanic Black and Hispanic male caregivers living with one or more chronic conditions.Design Data were analyzed from 418 male caregivers collected through Qualtrics Online Panels using an internet-delivered survey instrument (55.7% non-Hispanic Black, 44.3% Hispanic). Three ordinal regression models were fitted to assess factors associated with Caregiver Strain Scale tertiles: one for all men, one for non-Hispanic Black men only; and one for Hispanic men only.Results Similarities and differences were observed between the two groups in terms of factors associated with higher caregiver strain (i.e. lower disease self-management efficacy scores, providing ≥20 h of care per week). Uniquely for Non-Hispanic Black male caregivers, higher caregiver strain was associated with living with more children under the age of 18 (β = 0.35, P = 0.011) and feeling more socially disconnected (β = 0.41, P = 0.008). Uniquely for Hispanic male caregivers, higher caregiver strain levels were associated with experiencing lower pain levels (β = −0.14, P = 0.040) and higher fatigue levels (β = 0.23, P < 0.001).Conclusion Findings from this study suggest that non-Hispanic Black and Hispanic men with chronic conditions have differing caregiving experiences. While bolstering social connectedness and caregiver support services may offset caregiver strain, tailored mental health and disease management programming are needed to meet the specific needs of non-Hispanic Black and Hispanic male caregivers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".