Diversity, Equity and Inclusivity‐ Low and Middle Income countries and the Lancet commission
Bibliographic record
Abstract
Abstract Background Data from high‐income countries (HICs) suggest a decline in age‐specific incidence rates of dementia. However, this has happened primarily in HICs, with low‐ and middle‐ income countries (LMICs) facing two main challenges: a higher burden of risk factors and, in general, a faster ageing population. Most people with dementia live in LMICs, and this is set to increase, thus requiring urgent and robust action to prevent, treat and support people with dementia and their families. Methods We, in the Lancet Commission, reviewed the most recent literature, and conducted a new metanalysis on worldwide dementia risk. We have calculated worldwide figures, although there may be variation in different ethnic and socioeconomic groups. Results Dementia studies are overwhelmingly from HICs, and there is a tendency to recruit people of European origin with higher education and socioeconomic status, with few people from ethnic minority groups. The same applies in respect of worldwide clinical trials, including multicomponent interventions to reduce risk, biomarker research and pharmacological/non‐pharmacological interventions. Although most interventions are developed in HICs, culturally adapted interventions seem to be as effective in LMICs as in their original context, as long as the interventions’ core components are not compromised in the adaptation process. Conclusion Policy interventions can improve dementia prevention, particularly in LMICs and in minority and lower level socio‐economic groups ‐ precisely the people who have the greatest burden of modifiable risk and are more likely to develop dementia. Dementia prevention efforts should be tailored to the needs of the different countries and different groups within countries. Trials and research databases should aim for sociodemographic diversity to reflect real life populations. Although evidence‐based interventions developed in HICs can be effective in LMICs, there is often a lack of healthcare infrastructure and resources to deliver them. Moreover, cultural differences can make them inappropriate or less effective. Interventions should be developed in partnership with local communities to ensure their appropriateness in respect of the culture, beliefs and practises which vary within and between countries.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".