Demonstrating a Technology-Mediated Intervention to Support Medication Adherence in Community-Dwelling Older Adults in Primary Care: A Feasibility Study
Bibliographic record
Abstract
Background: Medication non-adherence can lead to significant morbidity and mortality. This 4-week feasibility study aims to demonstrate that the eDosette intervention can be implemented with older adults in primary care. Method: Fifty-six older adults from four primary care sites in Southwestern Ontario, Canada participated. The intervention involved generating, for pharmacist review, weekly medication administration records based on transmitted data captured by the eDosette. The primary outcome is implementation feasibility defined by recruitment, adherence rates, frequency of captured missed and late doses, descriptions of clinical work resulting from the intervention, and participant feedback. Results: The recruitment rate was 24% (57/240); one withdrew due to personal reasons. The mean observed adherence rate was 82% (range 49%-100%). Overall, participants missed 505 and took 2,105 doses late; 118 clinical decisions occurred with 72 unique medication changes in 31 participants. Participants found the eDosette easy to use and did not feel that they were viewed negatively because of their potential non-adherence. Conclusion: The eDosette intervention could be feasibly implemented in primary care with older adults. Providing information about when an older adult takes their medications could play a role in medication adherence by prompting more informed discussions between the older adult and primary care clinicians.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".