Co‐designing toolkits to improve post‐diagnostic support for people living with dementia, care partners and health and social care professionals (COGNISANCE)
Bibliographic record
Abstract
Abstract Background COGNISANCE is an international research programme (Australia, UK, Canada, Netherlands, and Poland). In partnership with people living with dementia, informal care partners, health and social care professionals and key national and international dementia organisations and researchers, we have co‐designed online toolkits aiming to improve post‐diagnostic support for dementia. Methods We have worked closely with local working groups representing members from key audiences and a design and marketing agency to run a series of workshops in five countries. The workshops to date have focussed on the key messages, motivators for information seeking, experiences of dementia diagnosis and post diagnostic support, the purpose for toolkits and the tone and branding appropriate for the key audiences for a resource that focusses on the first twelve months post‐diagnosis. Results Co‐design workshops were successfully run concurrently in five partner countries. Each country’s research team and local working groups remained highly engaged throughout the process. Key motivators for the toolkits led to a focus on a practical and empathetic resource that was tailored to the individual. The toolkits will be a website that has three separate pathways, one for people recently diagnosed with dementia, one for care partners and one for health and social care professionals. These will function to support communicating the diagnosis, post‐diagnostic support and planning for the first year post diagnosis. The design and marketing agency have worked closely with research teams and local working groups throughout the co‐design process to interpret and build iteratively on each workshop outcome. From this we have successfully produced a generic website that can be tailored in different locations to the three key audiences. Conclusion In the co‐design process, representative users identified the need for a practical, empathetic and individually‐tailored resource. The toolkit will be a website that has an individual planning tool for the first twelve months following a dementia diagnosis. We are continuing the co‐design process to develop a campaign. This will promote the key messages and toolkit, to plan for a life with dementia, ahead of user testing, implementation and evaluation.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".