Designing an overview Theory of Change for a multi-component support community for people affected by rare dementia
Bibliographic record
Abstract
Introduction: There is growing awareness of people living with diverse dementia syndromes, many of whom are younger in age, with distinct support needs. Planning for increasing numbers of people living with dementia and subsequent models of support has largely overlooked this population. To address this gap, the aim was to design a Theory of Change for multi-component rare dementia support. Methods: Intervention development frameworks underpinned the construction of a Theory of Change informed by research evidence on rare dementia support and an iterative consultation process with people with lived experience, researchers, educators and health and social care practitioners. Results: The Theory of Change illustrates pathways to activities for continuous and tailored support solutions, education and knowledge production. Characteristic features include relationship, connection and continuity for people with lived experience, training and networking for professionals, and relational support with a commitment to ongoing learning for the rare dementia support team. Conclusion: The Theory of Change is positioned to flexibly support people affected by rare dementia, strengthen capacity within all sectors, improve service quality whilst maintaining a commitment to knowledge production and mobilization.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".