Conceptualizing citizenship in dementia: A scoping review of the literature
Bibliographic record
Abstract
Citizenship has provided an important conceptual framework in dementia research and practice over the past fifteen years. To date, there has been no attempt to synthesize the multiple perspectives that have arisen in this literature. The purpose of this paper is to explore, reflect on, and contrast, the key concepts and trends in the citizenship discourse as it relates to people with dementia. Using a scoping review methodology, forty-nine articles were identified for review. Despite the use of different descriptors, thematic analysis revealed four core themes underpinning citizenship discourse: 1) the relationality of citizenship; 2) facilitated agency and autonomy; 3) attention to stigma, discrimination and exclusion; and 4) recognition of the possibilities of identity and growth. Overall, this scoping review found a major emphasis on expanding definitions of agency and autonomy to render citizenship unconditional and inclusive of the diverse life experiences of people living with dementia. Notably, there is recognition that a more intersectional lens for embedding the subjective experience within a broader socio-political context is needed. Whilst the adoption of a citizenship lens in dementia research and practice has had real-world implications for policy and research, its exploration and use continue to be led by academics, highlighting the importance that future research involve input form people with dementia.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".