Pharmacist Led Intervention on Inhalation Technique among Asthmatic Patients for Improving Quality of Life in a Private Hospital of Nepal
Bibliographic record
Abstract
PURPOSE: Asthma is a chronic disease which cannot be cured but can be controlled. Although drug therapy is used to relieve and prevent symptoms of asthma and treat exacerbations, still a good asthma control and a better quality of life in many patients is suboptimal due to improper use of inhalation technique. Thus, this interventional study was conducted to evaluate the effect of a pharmacist intervention on asthma control, quality of life and inhaler technique in adult asthmatic patients. PATIENTS AND METHODS: A total of 72 patients who met the inclusion criteria and agreed to give written consent were enrolled in the study. These patients were randomly divided into two groups i.e., test group (36) and control group (36) by simple block randomization technique. Test group were the interventional groups. Mini Asthma Quality of Life Questionnaire (AQLQ), Asthma Control Questionnaire (ACQ) and structured questionnaires were used to sort the information like quality of life, asthma control and demographic details. They were counselled by the pharmacist about the asthma management and proper use of inhalers. Out of 72 patients, only forty six patients came for follow up after one month. Data were entered and analyzed using Statistical Package for Social Sciences (SPSS) software version 20. RESULTS: = 0.099). Inhalation technique was found to be improved significantly after intervention among patients using the metered dose inhaler and dry powder inhaler. Majority of the patients were prescribed with Methylxanthines (24.5%) followed by combined Beta 2 agonists and Inhaled Corticosteroids (21.7%). CONCLUSION: Pharmacist provided intervention improves the quality of life, asthma control and inhalation technique among asthmatic patients.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".