Demography, diagnosis and alcohol-related brain damage
Bibliographic record
Abstract
This is a book about knowledge, ideas, skills and expertise for social workers. However, in order to understand our task we have to understand the context in which we work. Demography is a very important part of the context. All developed countries have rapidly ageing populations, which means that dementia, which is so closely linked to age, may be called the key health issue for the 21st century. We discuss demographics and then take a medical approach to dementia, which social workers need to understand. We include a special section on alcohol-related brain damage (ARBD) because social workers will be working increasingly with people with this condition. Over 750,000 older people in the UK have a diagnosis of dementia. Using population figures for 1996, this can be broken down as shown in Table 2.1. The primary risk factor for dementia is age. As social workers, we need to be aware, therefore, that demography is a key factor in our field because it affects both current services and future planning. There is a great deal of research in progress looking at risk factors such as family history, diet, stress and head trauma (Gow and Gilhooly, 2003). So far it is inconclusive although it is clear that the risk factors for vascular dementia are related to those for other vascular problems, like smoking, poor diet and little exercise. There is increasing attention to the possibility that there is a vascular component to all dementias (Snowden, 2001). A large study published in 2004 compared prevalence rates in nine Organisation for Economic Co-operation and Development (OECD) countries – Australia, Canada, England and Wales, France, Germany, Japan, Spain, Sweden and the US.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".