Bibliographic record
Abstract
There is considerable evidence that the combined impact of having a dementia and the negative response to diagnosis and symptoms significantly undermines psychosocial well-being and quality of life. There is growing evidence that stigma emphasizes and deepens this distress, yet we still know very little about the stigma experienced by persons with dementia (PwD) and their care partners. In this symposium, participants will learn about the types of stigma and how the experience of stigma interferes with attaining appropriate services, lowers quality of life, is associated with many mental health issues in PwD, and may be affected by novel advances in early diagnosis. In addition, coping styles and years of education may influence the experience of stigma by care partners. Attendees will learn that stigma is pervasive worldwide and may be more severe in some ethnic groups. Through data collected from an ongoing blog, participants will get an opportunity to hear personalized stories from PwD that highlight who they were before the disease and how things have changed. Participants will also understand the considerable variability in the general public’s knowledge of dementia which can contribute to the experience of stigma by PwD and their care partners. Stigma assessment tools and experimental approaches to assess or manage stigma will be reviewed. Interventions will be discussed that may lower the stigma experience including the results from a pilot study that introduces educational curriculum to address dementia-related 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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".