It’s all about the attitude: Findings on internalized caregiver stigma and mental health of informal caregivers
Bibliographic record
Abstract
OBJECTIVES: In this study, we aim to explore the associations between internalized care-related stigma, positive aspects of caregiving, caregiver burden, and mental health of informal long-term caregivers for older adults. METHODS: Data from the project, Attitudes Towards Informal Caregivers, collected in December 2023 in Germany, was used, including 433 informal long-term caregivers of adults aged ≥60 years. The Internalized Care Stigma Scale measures positive and negative attitudes toward informal care for older adults (aged ≥60 years) as internalized by informal caregivers. Additionally, the Burden Scale for Family Caregivers short scale, the short Patient Health Questionnaire, and the Positive Aspects of Care Scale (PAC) were used. Linear regression analysis with robust standard errors and path analysis with bootstrapped standard errors were conducted. The models were adjusted for sociodemographic background and care-related factors. RESULTS: Regression analyses showed an association between stronger negative care stigma beliefs and higher burden and lower positive aspects of care, while stronger positive care stigma was associated with higher burden and higher positive aspects of care. Path analyses revealed significant direct and indirect effects (via PAC) of both positive and negative attitudes on burden. Also, positive and negative care stigma were associated indirectly with poorer mental health, via PAC and burden. DISCUSSION: Care-related stigma played a significant role in caregivers' stress appraisal and mental health. Informal caregivers may benefit from actions targeting internalized negative care-related stigma, while further investigation of the more complex associations between positive care--related stigma and caregivers' mental health is recommended.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.002 |
| 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".