Digital tools for delivery of dementia education for health-care providers: a systematic review
Bibliographic record
Abstract
Continuing education on dementia for health-care providers has been shown to have positive effects on diagnostic confidence, knowledge, and care management. Technological approaches to educational delivery have been found to have comparable effects in terms of quality and efficacy. The purpose of the systematic review was to compose and present an evidence base for technology-delivered dementia education for health-care providers. The review used PRISMA guidelines and Cochrane methods focusing on studies with a pre- and post-intervention evaluation. Technology-based delivery of dementia education was broadly defined as any technology-based medium delivered in real time or asynchronously. Ten studies were identified and analyzed using content analysis. The review revealed positive outcomes post-intervention, for dementia knowledge, readiness to change, receptiveness to training, communication skills, and self-efficacy. Studies were rated as medium to high quality on a scale for measurement of published data in research, and there was generally an unknown risk of bias due to a lack of a control group in most studies (N = 7). The findings revealed benefits of digitally-based, asynchronous continuing education for health-care providers, which allow schedule flexibility and the ability to deliver remotely. Findings also revealed benefits of presentations using a variety of interactive educational materials via videos, voice recordings, textual medium and online discussion groups. Suggestions for intervention improvements include tailoring training for the specific needs and knowledge levels of health-care practitioners and using validated scales to measure 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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".