Impact and Perceived Value of iGeriCare e‐Learning Among Dementia Care Partners and Others: Evaluation Using the IAM4all Questionnaire
Bibliographic record
Abstract
BACKGROUND: Dementia is a growing global health issue; informal care partners provide the majority of care for individuals living with dementia, often at significant personal, emotional, and financial cost. Care partners often lack access to appropriate educational resources and support systems. Web-based educational interventions have the potential to support care partners, but existing programs are often limited by scope, language, or accessibility. METHOD: iGeriCare.ca, an evidence-based, freely available, internet-based program designed to educate dementia care partners at their own pace, was developed by experts from McMaster University. iGeriCare features 12 multimedia lessons, email-based micro-learning, and live events with expert interaction. Uptake and engagement are measured using web and learning analytics, and the IAM4all questionnaire is used to assess web-based health information outcomes. RESULT: Since July 2018, iGeriCare has over 227,000 unique users, more than 337,800 sessions, and 615,000 page views. The lessons have been accessed over 58,600 times. The 52-week email-based micro-learning series has had more than 3,050 subscribers. Thirty-eight live events have been hosted and recordings have been watched > 52,600 times. Since March 2021, 1,077 IAM4all responses have been received from care partners (38%), those with dementia or concerned about their cognitive health (29%), and health care providers/trainees (24%). 94% found the lessons relevant, and 99% reported understanding the content well. 61% reported learning something new, and 58% felt motivated to learn more. Respondents also reported feeling validated (50%), reassured (46%), or said the content refreshed their memory (37%). 98% reported intention to use a lesson, including: to better understand something (73%), discuss the information with someone else (53%), or do things differently (38%). 95% expected to benefit from the information, including to improve their health (52%), another person's health (51%), or handle a problem (51%). CONCLUSION: iGeriCare represents an innovative and scalable solution to meet the educational needs of care partners. By leveraging evidence-based instructional design, iGeriCare empowers care partners with the knowledge and confidence to manage their responsibilities effectively. Future analyses of a recently completed pilot randomized controlled trial will further explore its impact on care partner outcomes.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".