Mobile Early Detection Memory Screening project with Older Adults in Armenia utilizing the Montreal Cognitive Assessment Test (MoCA) in cooperation with mobile eyecare hospital.
Bibliographic record
Abstract
Abstract Background The aim of the project is to address this tremendous gap in care by developing a unique multidisciplinary in‐home mobile clinic to address the need for cognitive screening as well as provide overall healthcare for the person with Alzheimer’s and their families. The Brain Health Armenia Project is an innovative approach to addressing the need for comprehensive cognitive screening as well as the overall care of the person with dementia in Armenia. This country wide project will increase cognitive screening rates and proper diagnosis as well as visibility and awareness of Alzheimer’s disease which will help decrease myths and fears about the disease. Method cognitive screenings by partnering with the Armenian EyeCare Project by assessing patients for cognitive screenings. The Armenian EyeCare Project is a Mobile Eye Hospital that provides care for people throughout Armenia since 2002. They also have five Regional Eye Centers. The Brain Health Armenia Project will provide cognitive screenings to people who come to the mobile Eye Hospital as well as to their Regional Eye Centers. Once screened, people who show impairment twill be offered to be part of the Brain Health Armenia Project. The Brain Health Armenia Project’s multidisciplinary team will make in home visits where they will administer assessments, provide referrals to neurologists; provide cognitive rehabilitation, care management and follow‐up; palliative and end‐of‐life care. The team will provide caregiver education and counseling about the disease as well as best practices for caring for their loved one.The target population is vulnerable adults living in Armenia’s regions outside of main city. Result Currently, around 14.4% of Armenia’s population is already over the age of 60 (Ryan, 2018). By 2050, 31.5% of Armenia’s population will be over the age of 60 (Ryan, 2018). The number of people in Armenia with Alzheimer’s disease is expected to grow exponentially in the coming decades. Conclusion This Project, which the Ministry of Health endorses, will increase the importance of cognitive screening as well as care of the person with dementia and be a significant program for the country’s National Dementia Plan. This project has revealed the prevalence of dementia in Armenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".