UnderstandingMCI.ca: Mixed-Methods Evaluation of a Brief Web-Based Multimedia Lesson to Improve Public and Family Care Partner Knowledge of Mild Cognitive Impairment
Bibliographic record
Abstract
Mild cognitive impairment (MCI), also known as mild neurocognitive disorder, represents a transitional stage between normal cognitive aging and dementia and often signals early neurodegenerative change. Despite its clinical importance, MCI remains poorly understood by the public and family care partners, leading to uncertainty and distress following diagnosis. This study evaluated UnderstandingMCI.ca, a brief multimedia e-learning lesson designed to improve MCI literacy among the public and care partners. The lesson was disseminated through the McMaster Optimal Aging Portal, with web analytics tracking uptake, progress, and completion, and a post-lesson survey incorporating the Net Promoter Score (NPS), the Information Assessment Method for all (IAM4all) questionnaire, and open-text feedback assessing perceived impact. Between January 15 and February 7 2025, over 5,000 users initiated the lesson, 1,537 completed it, and 984 responded to the survey. Respondents were predominantly women aged 65 years or older. The NPS was 72 (“excellent”); 942 respondents (96%) found the lesson relevant, 937 (95%) anticipated benefits from using the information, and nearly all (982 respondents) reported understanding the material. Thematic analysis of 296 comments identified greater understanding of MCI versus normal aging and dementia, emotional reassurance, and motivation for proactive brain-health behaviors. UnderstandingMCI.ca appears to effectively improve public MCI literacy and confidence, offering a scalable, accessible tool for education in clinical and public health contexts.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".