Development and Validation of the <i>Internalized Care Stigma Scale</i> (ICSS): Measuring care stigma aimed at informal care for older adults in Germany
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In this study, we developed and validated the Internalized Caregiver Stigma Scale (ICSS) to measure internalized stigma targeting informal care for older adults (≥60 years) in Germany. RESEARCH DESIGN AND METHODS: The ICSS scale was developed in the Attitudes Towards Informal Caregivers project based on stigma theories and (cognitive) pretesting with informal caregivers. Informal long-term caregivers (aged ≥40 years; n = 433) of older relatives (aged ≥60 years) were quota-sampled from the online panel GapFish in December 2023 (twice as many female and middle-aged [aged 40-64 years] caregivers than male and younger [18-39 years] or older adults [65+ years] were included in the sample). Caregiver identification and the Social Impact Scale were used for validation of the newly developed measure. Factor structure, reliability, and concurrent validity were tested. RESULTS: A correlated 2-factor model with excellent goodness-of-fit criteria and good to excellent internal consistency of the factors and the total scale was confirmed for the ICSS. The negative ICSS subscale correlated highly, and the positive ICSS subscale correlated weakly, with the care-specific Social Impact Scale. Both ICSS subscales were weakly correlated with caregiver identification. Both aspects of self-stigma showed significant associations with sociodemographic and care-specific factors in the regression models. DISCUSSION AND IMPLICATIONS: The ICSS is the first scale measuring internalized stigma targeting informal care for older adults directly and shows excellent psychometric criteria. It provides the necessary tool for a new approach to analyze the complex psychosocial mechanisms in this highly relevant care context.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".