DEMENTIA-RELATED STIGMA IS ALIVE AND NOT WELL AMONG UNREGULATED HOME CARE WORKERS
Bibliographic record
Abstract
Abstract Dementia-related stigma is pervasive despite the growing rates of dementia globally. The social cognitive model of stigma contends that stigma is created and maintained through negative stereotypes, prejudice, and discrimination towards people who belong to a group. Research on dementia-related stigma highlights the prevalence of stigmatizing views among formal care providers. However, little is known about how stigma is created and maintained by the ways in which frontline care providers talk about persons living with dementia. Accordingly, this study aimed to identify ways in which stigmatizing language is used by home care workers when describing routine care interactions with clients living with dementia. Semi-structured interviews were conducted with 30 unregulated home care workers, who shared their experiences caring for clients with dementia. We used conventional content analysis to identify themes related to dementia-related stereotypes, prejudice, and discrimination. Under stereotypes, persons with dementia were portrayed as objects, infants, not engaging, and cognitively and behaviorally unstable. Under prejudice, persons with dementia invoked pity, fear, disdain, and emotional fatigue. Under discrimination, participants shared experiences of excluding/ignoring, controlling, and using patronizing communication with persons living with dementia. Uncovering common examples of stigmatizing language offers opportunities to train home care workers to use person-centered communication. It is noteworthy that stigmatizing language emerged, when not asked about directly. Our findings underscore the persistence of dementia-related stigma and the need for training to eliminate stigma.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".