Best Practices & Qualities of Recreational Dementia Friendly Reading Materials
Bibliographic record
Abstract
Dementia friendly communities have been gaining increased recognition over the years. These communities play a large role in creating opportunities and welcoming spaces for people with dementia. The creation of dementia friendly communities has led to increased development in meaningful dementia friendly experiences including public outings, accessible services, and the creation of dementia friendly reading groups. Despite the effects of dementia, many people with dementia retain their ability to read, even if it is as a lower level. However, reading has remained largely ignored as a meaningful experience for people with dementia. Additionally, there are few resources regarding the creation of dementia friendly reading materials and what is required in order for them to be effective. As a result, there is a lack of appropriate and mature reading materials available for people with dementia. The goal of this study is to determine the necessary qualities of recreational dementia friendly reading materials, through a literature review and case study, and provide best practices for the development of future dementia friendly reading materials. There is a notable lack of research conducted in the library and information studies field, and related fields, regarding recreational dementia friendly reading materials. This study will assist in filling in a gap in the literature and help to establish basic criteria for creating appropriate dementia friendly reading materials.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".