Remote Learning for Adults with Mild Cognitive Impairment in the New Landscape of COVID-19 Restrictions
Bibliographic record
Abstract
Introduction: Mild Cognitive Impairment (MCI) is a transitional stage between the expected cognitive decline of normal aging and the more serious decline of dementia. Research interest focuses on the protection of this transition, through a combination of therapeutic interventions. Μaterials and Methods: 45 individuals with MCI who were followed up by the interdisciplinary team of our Memory School attended an online remote educational program that consisted of a series of cognitive and occupational therapy interventions designed to maintain their cognitive, executive, motor and emotional skills during the COVID-19 period. At baseline and at twelve-month follow-up all patients underwent several neuropsychological tests that included the Montreal Cognitive Assessment (MoCA), the Mini Mental State Examination (MMSE), the Functional Cognitive Assessment Scale (FUCAS), the Geriatric Depression Scale (GDS) and the Functional Rating Scale of Symptoms of Dementia (FRRSD). Also, the Barthel Index scale and the Timed Up and Go (TUG) test were utilized to assess functionality and mobility, respectively. Results: Within twelve months participants’ performance did not change significantly (MoCA; p=0.908, MMSE; p=0.625, FUCAS; p=0.782; GDS; p=0.218, FRSSD; p=0.18, Barthel Index; p=0.317, TUG; p=0.68). Results confirmed the hypothesis that cognitive, executive, motor and behavioral skills can be maintained through an online educational protocol during the COVID-19 lockdown period. Conclusions: It is proposed that similar educational protocols should be included in the design of strategic directions in the field of healthcare in MCI and dementia, especially in health crisis situations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".