Dementia research in Ireland: What should we prioritise?
Bibliographic record
Abstract
Background: Dementia research prioritisation allows for the systematic allocation of investment in dementia research by governments, funding agencies and the private sector. There is currently a lack of information available in Ireland regarding priority areas for dementia research. To address this gap, a dementia research prioritisation exercise was undertaken, consisting of an online survey of professionals in the dementia field and workshops for people living with dementia and family carers. Methods: (1) An anonymous online survey of professionals, based on an existing WHO global survey: The global survey was adapted to an Irish context and participants were asked to score 65 thematic research avenues under 5 criteria; (2) A mixed-methods exercise for people living with dementia and family carers: This involved two facilitated workshops where participants voted on the research themes they felt were important to them and should be addressed through research. Results: Eight of the top ten research priorities in the survey of professionals ( n =108) were focused on the delivery and quality of care and services for people with dementia and carers. Other research avenues ranked in the top ten focused on themes of timely and accurate diagnosis of dementia in primary health-care practices and diversifying therapeutic approaches in clinical trials. Participants in the workshops ( n =13) ranked ‘better drugs and treatment for people with dementia’, ‘dementia prevention/ risk reduction’ and ‘care for people with dementia and carers’ as their top priority areas. Conclusions: Findings from this prioritisation exercise will inform and motivate policymakers, funders, and researchers to support and conduct research to address the burden of dementia and ensure that the limited resources made available are spent on research that has the most impact for those who will benefit from and use the results of research.
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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 | 0.241 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.045 | 0.050 |
| Open science | 0.010 | 0.030 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".