Development and validation of the Perceived Care Stigma Scale (PerCSS): measuring cognitive, emotional and behavioral reactions to (unpaid) caregivers of older family members and friends
Bibliographic record
Abstract
OBJECTIVES: This study reports the development and validation of the first scale to measure perceived care stigma among unpaid (family and friends) caregivers of older adults based on a social stigma framework. METHODS: Based on data from the Attitudes Towards Informal Caregiving (ATTIC) project collected in Germany in December 2023, a sample of 433 unpaid caregivers of older relatives or friends (65+ years) was questioned with an online survey. Supported by a group of informal caregivers, the Perceived Care Stigma Scale (PerCSS) was developed. The PerCSS was tested for content and concurrent validity, factor structure and reliability by using two subsamples and conducting exploratory and confirmatory factor analyses. RESULTS: ) showed good to excellent internal consistency based on McDonald's omega. They showed high concurrent and discriminant validity when compared to the Warmth-Competence Scale and the Social Impact Scale. DISCUSSION: This study developed and tested the first instrument to measure stigmatization of informal care for older adults as perceived by caregivers in their close social network (family and friends). This provides the basis for further research on the impact perceived care stigma has on caregivers' well-being, decision-making and care performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".