Developing the dementia workforce: Numerus turbatio - ‘Total confusion’
Bibliographic record
Abstract
Dementia is a global issue, which is eliciting increasing concern around the world. Ferri et al. (2005) examined the global prevalence of dementia and estimated that it affected 24.3 million people and that the number of people involved would double every 20 years to 81.1 million by 2040. They pointed out that most people with dementia live in developing countries (60% in 2001, increasing to 71% by 2040). Across the developed world this challenge is leading to political action. The European Parliament has adopted a proposal for a European initiative on Alzheimer’s disease and other dementias (Commission of the European Communities, 2009), and the Alzheimer Europe website (http://www.alzheimer-europe.org/Policy-inPractice/National-Dementia-Plans) summarizes the situation regarding dementia plans in Europe, e.g., Norway published a Dementia Plan in 2007 (Norwegian Ministry of Health and Care Services, 2007). Elsewhere the Australian Health Ministers’ Conference produced a Framework for Action of Dementia in 2006 (New South Wales Department of Health, 2006). Other parts of the world are lagging behind. In the United States the Alzheimer’s Study Group (2009) issued a call that America must act now. The Alzheimer Society of Canada (2010) has just published Rising Tide: the impact of dementia on Canadian society, and calls for government action to make dementia a national priority. In England the National Dementia Strategy (Department of Health, 2009) highlighted the importance of the dementia workforce, and introduced the new role of dementia adviser. Objective 4 sets out what this might involve: a ‘dementia adviser to facilitate easy access to appropriate care, support and advice for those diagnosed with dementia and their carers’ (page 11). The Strategy acknowledges the need for demonstration projects, and for the model to be piloted and evaluated before widespread implementation. Despite attempts to develop strategy at national and regional level, individuals are being appointed on the ground to a wide variety of posts as ‘advisers’, ‘navigators’, ‘coordinators’ and others; employed by a wide variety of organizations, statutory and third sector, health and social care, and in different settings; both primary and secondary care. Our experience,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".