Improving Asthma Management: Patient–Pharmacist Partnership Program in Enhancing Therapy Adherence
Bibliographic record
Abstract
Community pharmacist interventions can assist in improving adherence in patients with asthma. The objective of the study was to assess the feasibility of patient-centered counseling using the developed asthma-specific tools to identify barriers to adherence and identify their preliminary effect on adherence barrier score and asthma control. Adult patients with persistent asthma were invited to participate in a 3-month pre-post intervention study involving community pharmacist-provided patient-centered counseling. Bivariate analyses were conducted to determine whether there were changes in outcomes from the pre to post period. Of 36 recruited patients, 17 completed both pre and post surveys. At baseline, patients had a mean ACT score of 15.1 ± 3.5, with 94% having uncontrolled asthma, and an average of 4.2 ± 2.5 reported barriers. The following barriers were most common: not having an Asthma Action Plan (52.9%), use of inhaler more or less often than prescribed (47.1%) and forgetfulness (41.2%). The ACT score increased by 2.7 ± 5.4, which was not statistically significant; however, it might be clinically significant. Two barrier scores improved as a result of the intervention. Preliminary evidence on the feasibility of identifying and addressing patient-specific barriers to adherence delivered by pharmacists showed that it has the potential to resolve barriers and improve asthma outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".