Competence in metered dose inhaler technique among dispensers in Mekelle
Bibliographic record
Abstract
BACKGROUND: Inhaled medications are the cornerstone of asthma therapy. Metered dose inhaler technique is a widely used technique to administer medications like corticosteroids. Meanwhile, the health professionals and patients knowledge and practice towards this metered dose inhaler is quite deficient but arguably understood by policy makers or education expertise. OBJECTIVE: This study tried to assess the pharmacists and druggists competency on MDI who are the professionals at the front line to demonstrate and teach the technique for patients. METHOD: A cross sectional study was conducted among registered pharmacists and druggists from different public and private pharmacies and drug stores in Mekelle Town, Ethiopia from March to June, 2013. Evaluation tool was adapted from the National Asthma Education and Prevention Programmes of America (NAEPP) step criteria for the administration of a metered dose inhaler to score the knowledge/proficiency of use of MDIs by the subjects using two evaluators. RESULT: The mean score given by evaluators was 4.34 and 4.28 by evaluator I and II respectively. Of the 106 professionals took part in this research, based on the competency on essential steps for optimum therapeutic value of MDI, only 2 (1.9%) and 1 (0.9%) study participants had adequate competency in metered dose inhaler according to evaluator I and evaluator II respectively. The rest, irrespective of their age, sex, educational status and experience, did not achieve adequate score on MDI technique. Of the essential steps, only 25 (23.6%) and 16 (15.1%) participants breathed in and actuating the canister together according to evaluators I and II respectively. CONCLUSION: Very poor MDI technique was very common in this sample of healthcare providers. Despite involvement of all participants in patient counselling on inhalers, none of them were able to perform all steps correctly, which shows that patient may not have adequate instruction.
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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 | 0.000 |
| 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".