Users’ Perceptions of an in-Home Electronic Medication Dispensing System: A Qualitative Study
Bibliographic record
Abstract
Armghan Ahmad, Venus Chiu, Mubashir Aslam Arain Alberta Health Services, Health Systems Evaluation and Evidence, Calgary, AB, CanadaCorrespondence: Mubashir Aslam ArainAlberta Health Services, Health Systems Evaluation and Evidence, 10301 Southport Lane SW Calgary, AB T2W 1S7, CanadaTel +1 403-943-0783Fax +1 403-943-2875Email mubashiraslam.arain@ahs.caBackground: Managing and taking multiple medications as prescribed can be a difficult task for older adults. In-home medication dispensing technologies could help enhance care. The objective of the study was to determine users’ perspectives on a medication dispensing system (MDS) in supporting medication adherence of individuals living at home with chronic conditions.Methods: This analysis is a part of a randomized controlled trial on an MDS in a Western Canadian province. We interviewed participants who were recruited into the intervention group and started using an MDS. A maximum variation purposive sampling was used to select interview participants based on age, number of medications, and health conditions.Results: Thirteen participants were interviewed; most participants were females (n=11) and the average age was 63.7 (SD=8.2) years with an average of 8.9 (SD=3.6) prescribed medications. The most common health conditions were hypertension, diabetes, arthritis, and anxiety and depression. Four main themes emerged from thematic analysis: MDS acceptability, MDS patient support, need for the MDS, and areas of technology improvement. Most of the participants found the MDS to be acceptable and convenient, although privacy and security was an issue for some older adults. Audio and visual reminders and pre-organized medication supported participants’ medication adherence and independence in daily routines. The perceived necessity of the MDS was split among participants with cost being one of the main concerns. Areas of technology improvement included the hard-to-open plastic medication packets and the sometimes inexact recording of medication adherence by the MDS if medications were dispensed on behalf of the patients.Conclusion: The MDS is an acceptable tool for improving medication management and adherence in older adults. Increased medication adherence may lead to patient and system-level benefits.Keywords: medication dispenser, qualitative research methods, patient experience and preferences, aging and seniors, polypharmacy, chronic conditions
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".