Dementia in the Chinese Population and the Potential of Musical Treatment.
Bibliographic record
Abstract
In terms of the targeting population, research of cognitive performance tends to cluster much more towards the younger end of the lifespan, where scholars put much emphasis on how cognitive abilities develop rather than decline. The Chinese population has been contributing the largest proportion of people with dementia more than any other regions in the world, and such situation will get even severer in the near future as reported by Ferri et al (2006) and Rodriguez et al (2008). In this regard, a more thorough investigation into the dementia problem in the Chinese population is in urgent need. Among all the cognitive screening tools for dementia, MMSE (Mini–mental State Examination) and MoCA (Montreal Cognitive Assessment) are the most heavily-adapted ones, yet in their Mandarin and HK-Cantonese versions, there are not a few linguistic bias which should be paid sufficient attention to. Another flourishing study area brought along by the dementia issue is musical treatment. The unique power of music as both cognitive reserve & healing tools has not only been reported in anecdotals but also manifested by increasingly more empirical evidence (Baird & Samson, 2015). Scholars has just begun to unlock this mysterious power and are expecting a new boom for the musical treatment in dementia. The aim of this study is three-fold: 1) to provide meta-analyses of dementia prevalence and its major risk factors (such as age, gender, and educational background, etc.) in the Chinese population; 2) to point out the problem of culture bias when adapting various screening tools for dementia in the Chinese population 3) to probe into the power of music as both cognitive reserve and healing device in dementia, with special attention to the uniqueness of Chinese music and the potential direction for music treatment tailored for the Chinese population.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".