Evaluation of a pharmacist-led intervention to improve medication adherence in patients initiating dabigatran treatment: a comparison with standard pharmacy practice in Poland
Bibliographic record
Abstract
BACKROUND: Dabigatran is a direct thrombin inhibitor used to treat cardiac arrhythmias, and rates of non-adherence to dabigatran in Polish populations are high. The current study examined how a pharmacist-led intervention of counselling with pictogram-enhanced medication instructions, and smartphone medication reminders, can improve adherence to dabigatran. METHODS: A 3-month pharmacist-led intervention was conducted in community pharmacies in Poland on 325 men and women filling a dabigatran prescription for the first time. Participating pharmacies were assigned into the Control Group (n = 172 patients) or the Intervention Group (n = 153 patients). The primary outcome of this prospective study was self-reported medication adherence assessed at 3 time points (day 7, day 21, and day 90) after initiation of dabigatran. RESULTS: Patients in the Intervention Group were significantly more adherent (mean days on Dabigatan/week) than the Control Group at 7 days (6.0 ± 0.9 vs 5.4 ± 1.1, p < 0.0001), 21 days (5.6 ± 1.0 vs 4.9 ± 1.3, p < 0.0001), and 90 days (5.5 ± 1.3 vs 4.4 ± 2.0, p < 0.0001), respectively. The percentage of patients in the Intervention Group who reported taking dabigatran twice/day as prescribed was significantly higher than the Control Group at 7 days (82.7% vs 71.4%, p = 0.0311), at 21 days (84.4% vs 58%, p < 0.0001), and at 90 days (78.4% vs 39.7%, p < 0.0001), respectively. The proportion of patients fully adherent (every day, twice/day) at 90 days was significantly higher in the Intervention Group than in the Control Group (26.1% vs 13.2%, p = 0.0145). CONCLUSIONS: Our findings support the role for interventions in community pharmacies in Poland to improve medication adherence, thus providing evidence for the efficacy of a pharmacist-led pictogram and smartphone-based program to support optimal dabigatran treatment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".