Implementation of a Real-Time Medication Intake Monitoring Technology Intervention in Community Pharmacy Settings: A Mixed-Method Pilot Study
Bibliographic record
Abstract
Innovative dispensing products offering real-time medication intake monitoring are being developed to address medication non-adherence. However, implementation of these interventions within the workflow of a community pharmacy is unknown. The purpose of this study was to explore factors affecting implementation of a real-time adherence-monitoring, multidose-dispensing system in community pharmacies. A mixed-method study was conducted with pharmacy staff, who packaged and dispensed medications in smart multidose packages and monitored real-time medication intake via web-portal. Pharmacy staff participated in semi-structured interviews. The Technology Acceptance Model, Theory of Planned Behaviour and Capability, Opportunity, Motivation, Behaviour Model informed the interview guide. Interview transcripts were analyzed thematically and findings were mapped back to the frameworks. The usability was assessed by the System Usability Scale (SUS). Three pharmacists and one pharmacy assistant with a mean of 19 years of practice were interviewed. Three themes and 12 subthemes were generated. Themes included: pharmacy workflow factors, integration factors, and pharmacist-perceived patient factors. The mean SUS was found to be 80.63. Products with real-time adherence monitoring capabilities are valued by pharmacists. A careful assessment of infrastructure-including pharmacy workload, manpower and financial resources-is imperative for successful implementation of such interventions in a community pharmacy setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".