How does ageism and familism contribute to our perspective of informal caregiving?: An analysis of informal caregiver stigma in Germany
Bibliographic record
Abstract
OBJECTIVES: In light of the rising demand for informal care, it is important to understand how attitudes towards family, aging and caregiving are associated. This study aims to explore attitudes towards older adults (ambivalent ageism) and family cohesion (familism) in association with the positive and negative attitudes towards informal care for adults aged 60 years and older (caregiver stigma) in Germany. METHOD: = 433 informal caregivers for adults aged 60 years and older from Germany were quota-sampled from an online access panel and questioned with the Ambivalent Ageism Scale, Short Attitudinal Familism Scale and the Internalized Care Stigma Scale. Multiple regression analysis with cluster-robust standard errors adjusted for sociodemographic background, level of care dependence and personality were conducted. RESULTS: Stronger ageism was associated with stronger caregiver stigma. Further analysis revealed stronger familism to be associated with higher levels of positive caregiver stigma, while stronger hostile ageism was associated with stronger negative caregiver stigma. CONCLUSION: Hostile attitudes towards older adults and beliefs in family cohesion are associated with caregivers' perception of informal care provision. This informs decision- and policymaking on aging and care, by highlighting the need to further decrease hostile ageism and negative informal caregiver stigma in society.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".