Living With Dementia: Flipping Stigma on Its Ear
Bibliographic record
Abstract
Abstract The language of social citizenship has emerged in the academic literature as one way of shifting the discourse to counter persistent problems of stigma and social exclusion for people with dementia. What this means and how it is experienced however from the perspective of those with dementia remains unclear. As part of a larger Participatory Action Research (PAR) study, an Action group of people with dementia began meeting in June 2019. The group now consists of ten members and meets monthly. The first task of the Action group was to assist in developing a more refined and practical understanding of the construct of social citizenship. Facilitated discussions were guided by the following questions: What are experiences of social citizenship by people with dementia? What kinds of practices and relationships promote the capacity of people with dementia to experience themselves as social citizens? Emerging findings indicate that the stigma is readily identified as a dominant aspect of the experience of living with dementia which needs to be ‘flipped on its ears’. Strategies for countering stigma include recognizing how language can both facilitate and block change, acknowledging dementia as a time of both loss and significant growth, remaining visible as a whole person – equal and also different - and maintaining active participation in one’s own life. These themes tie directly to the components identified in the academic literature of citizenship. However, members of the Action group were clear that the language of social citizenship is neither empowering nor strategic.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".