Training persons with early-stage Alzheimer’s disease how to use an electronic medication management device: development of an intervention protocol
Bibliographic record
Abstract
Abstract Background/Objectives: Medication management is challenging for persons with Alzheimer’s disease (AD) and their caregivers. Electronic medication management devices (eMMDs) are specifically designed to support this task. However, theory-driven interventions for eMMD training with this population are rarely described. This study aimed to develop and assess the appropriateness of an intervention protocol to train persons with early-stage AD how to use an eMMD. Methods: Interviews with three categories of participants [persons with early-stage AD ( n = 3), caregivers ( n = 3), and clinicians ( n = 3)] were conducted to understand medication management needs, perceived usefulness of an eMMD, and to explore training strategies. Subsequently, this knowledge was integrated in an intervention protocol which was validated with the three clinicians. A content analysis led to iterative modifications to maximize the acceptability and coherence of the intervention protocol in a homecare context. Results: The final intervention protocol specifies the expertise required to provide the training intervention and the target population, followed by an extensive presentation of eMMD features. Specific learning strategies tailored to the cognitive profile of persons with AD with step-by-step instructions for clinicians are included. Finally, it presents theoretical information on cognitive impairment in AD and how eMMDs can support them. Conclusions: This intervention protocol with its theoretical and pragmatic foundation is an important starting point to enable persons with early-stage AD to become active users of eMMDs. Next steps should evaluate the immediate and long-term impacts of its implementation on medication management in the daily lives of persons with AD and their caregivers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".