Social prescription for those living with dementia; does MedTech have a role to play?
Bibliographic record
Abstract
Ageing is the major risk factor for dementia and nearly every country has seen its life expectancy rise from the beginning of the 21st century. Remaining socially connected has positive health and social implications and may be even more significant for marginalized group of people like those living with dementia. If appropriately used, social prescriptions can help deliver value-based social engagement and primary care by maximising the utilisation of resources and addressing social determinants of health, decreasing dependency on the biomedical model and thus providing a way for health care systems to deal with social determinants of health. More frequently, however, those seeking access to these programmes do not tend to do so simply due to lack of understanding and knowledge of the availability of such services. So, provision of social activities involves more than developing a program and hoping people will attend, and considering the particular situations of those living with dementia as marginalised group of people, and taking into account that there is no treatment for dementia, societies need to move toward social prescription, integrating appropriate MedTech support- targeting on those living with dementia- into such programs.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".