The Preventing Dementia MOOC: Contribution to First Nations’ Health and Well-Being
Bibliographic record
Abstract
Abstract Dementia is a global public health issue. First Nations people are at increased risk due to complex intergenerational factors grounded in inequalities in health services and economic and educational opportunities. While there is yet no drug-related cure for this progressive and terminal neurological condition, evidence confirms that increased understanding of dementia and modification of lifestyle factors can reduce risk. The primary potentially modifiable risk factors are not completing secondary school, midlife hypertension, obesity, type II diabetes, depression, physical inactivity, smoking, hearing loss acquired after the age of 55 years, and social isolation. Inherent in these factors is stress, affecting mental health. Addressing these factors globally could prevent or delay over 40 million cases of dementia. The free Preventing Dementia Massive Open Online Course (PD MOOC) is a globally recognized 4-week course that aims to build self-efficacy in knowledge and management of modifiable risk factors. The course has reached over 68,000 people world-wide and is rated highly; however, its contribution to First Nations communities has not yet been investigated. We describe the content of the PD MOOC, report on its impact in a cohort of older Aboriginal people (≥ 50 years of age) in Circular Head, Tasmania, Australia six months after course completion, and emphasize the importance of including traditional approaches to healing. We describe a protocol in which cultural determinants of health can be infused into the PD MOOC and evaluated to promote health and well-being globally for older First Nations people.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".