Asthma knowledge and management practices of pharmacists in Greater Accra
Bibliographic record
Abstract
Background: Asthma continues to be a global public health problem, and community pharmacists are at the center of drug therapy in areas such as Greater Accra, Ghana. Methods: This study determined what 177 community pharmacists knew about managing asthma and what barriers they faced in their practice. Data were collected using a structured questionnaire regarding demographics, asthma knowledge, daily management practices, and perceived barriers. Results: Most pharmacists (55.93%) had a good understanding of asthma management, 42.37% had moderate knowledge, and 1.69% had poor knowledge, the results showed. More specific to asthma were pharmacists (100%), were well versed in asthma pathophysiology (100%), triggers (93.22%), and use of peak expiratory flow (PEF) meters (94.92%). However, some inconsistencies in practice were present. While inhalation techniques were assessed by 73.68%, treatment side effects were discussed by 79.31%; however, only 49.15% were routinely counselled on inhaled corticosteroids (ICS). In addition, only 18.64 percent ensured patients had written asthma action plans. Asthma Action Plans were lacking in 81.4%; there were inadequate training opportunities (64.4%); designated consultation areas were lacking (52.5%); pharmacists had insufficient time to care (52.5%) and had time constraints to care (55.9%). Our findings are consistent with global observations and highlight system-level challenges that impede effective asthma management. Conclusions: Although the Greater Accra pharmacists are knowledgeable enough based on their low rates of noncompliance indicated by their blood pressure scores, we believe that gaps in their practices and barriers to narrowing these gaps must be bridged.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".