Community Pharmacists’ Interventions in Asthma Care: A Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Factors influencing community pharmacists' interventions have been identified, but little information is available regarding these factors in asthma care. OBJECTIVE: To describe the type and frequency of pharmacists' asthma care interventions and to identify factors influencing those interventions. METHODS: A pretested, self-administered questionnaire was mailed to all community pharmacists registered with the Ordre des pharmaciens du Québec in 2006. The form included questions about the pharmacists' interventions in asthma care in the community setting (21 questions), factors influencing the provision of those interventions (13 questions), and the responders' characteristics (17 questions). RESULTS: A total of 4587 questionnaires were sent; 917 pharmacists returned the questionnaires (response rate 20%), and 877 were eligible for analysis. Overall, community pharmacists who completed the questionnaire appeared to intervene frequently when the initial prescription for asthma medication was filled. About 98% of responders reported providing verbal information always or often on new asthma medication prescriptions. Furthermore, checking for overuse of rescue medication and underuse of maintenance therapy always or often was reported by 91% and 85.8% of responders, respectively. Other interventions at follow-up were not as frequently reported. For example, only 8.4% of pharmacists reported reassessing inhalation technique always or often. Lack of time was reported to be an important barrier to the type and frequency of intervention, while interest on the part of the patient appeared to be a significant facilitator. About 99% of pharmacists agreed with the statement that they have an important role in asthma care. CONCLUSIONS: Community pharmacists appear to intervene with patients with asthma mostly at the initiation of treatment, but some interventions at follow-up are not frequently done, which could be attributed to organizational factors.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".