Examination of Barriers to Medication Adherence, Asthma Management, and Control Among Community Pharmacy Patients With Asthma
Bibliographic record
Abstract
Objective: To describe the prevalence of common barriers to asthma medication adherence and examine associations between patient-reported asthma controller adherence and asthma control, therapy adherence barriers, and asthma management characteristics. Methods: Previously developed asthma-specific tool was pilot tested on a convenience sample of adult patients with persistent asthma. The following data were collected via patient survey: demographic characteristics and comorbidities, adherence, asthma control, and asthma management characteristics. Descriptive and inferential statistics were used to address the study objective. Results: The patients (N = 93) were 45.4 (17.2) years of age, and 66.7% were female. The majority had poor (68.8%) adherence, with 61.3% of patients having controlled asthma. There was no significant association between adherence and asthma control. The mean number of barriers for good and poor adherence groups differed significantly: 2.0 ± 1.1 and 5.4 ± 2.4, respectively ( P < .0001). Having an asthma action plan (AAP) was the only asthma management characteristic significantly related to adherence. The majority of patients with poor adherence did not have an AAP (76.6%), whereas 81.5% of patients with good adherence did have an AAP ( P < 0.0001). Conclusions: The use of this survey tool confirmed presence of asthma-specific barriers, thus using this specialized approach may lead to more effective, targeted counseling in community pharmacy settings.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".