“It’s a Postcode Lottery”: How Do People Affected by Dementia in Wales Experience Their Diagnosis and Post-Diagnostic Support, and How May These Be Improved?
Bibliographic record
Abstract
National dementia strategies are government policies that guide the provision of appropriate support for people living with dementia. These strategies, developed through extensive stakeholder engagement, should be tailored to the cultural and demographic needs of a country. Using a mixed methods survey design, this study explored the aims of the Dementia Action Plan (2018-2022) for Wales (UK) around assessment, diagnosis, and post-diagnostic support, and assessed whether these are being realized. Further, it sought to gain insight from people living with dementia and their carers around how the experience may be improved for others in the future, as the development of the next iteration of the Action Plan is anticipated. Respondents included 71 people, affected by typical and rarer types of dementia, living in both rural and urban areas. Findings suggest both positive and negative experiences, reflecting a 'postcode lottery' of service provision. Attainable recommendations for improvement were made by respondents, which would ultimately likely be cost-effective and reduce strain on formal services. The findings reported in this paper concur with those reported by people living with dementia in other countries, indicating their relevance for policymakers beyond Wales.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".