Assessing the knowledge to practice gap: The asthma practices of community pharmacists
Bibliographic record
Abstract
BACKGROUND: Community pharmacists are well positioned to identify patients with poorly controlled asthma and trained to optimize asthma therapy. Yet, over 90% of patients with asthma live with uncontrolled disease. We sought to understand the current state of asthma management in practice in Alberta and explore the potential use of the Chat, Check and Chart (CCC) model to enhance pharmacists' care for patients with asthma. METHODS: An 18-question survey was used to examine pharmacists' monitoring of asthma control and prior use of the CCC tools. Descriptive statistics were used to characterize the response rate, sample demographics, asthma management and CCC use. Survey validity and reliability were established. RESULTS: One hundred randomly selected pharmacists completed the online survey with a 40% (100/250) response rate. A third of responding pharmacists reported talking to most patients about asthma symptoms and medication, with a greater focus on talking with patients on new prescriptions over those with ongoing therapies. Fewer than 1 in 10 pharmacists routinely talked to most patients about asthma action plans (AAPs). The majority of pharmacists (76%) were familiar with the CCC model, and 83% of those reported that the CCC model influenced their practice anywhere from somewhat (45%) to a great deal (38%). Both scales had good reliability, and factor analysis provided support for scale validity. CONCLUSIONS: There was considerable variability in pharmacists' activities in monitoring asthma. Pharmacists rarely used AAPs. The CCC model had a high level of self-reported familiarity, use and influence among pharmacists.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".