Effectiveness of Nurse-Driven Inhaler Education on Inhaler Proficiency and Compliance Among Obstructive Lung Disease Patients: A Quasi-Experimental Study
Bibliographic record
Abstract
Background Health education on proper inhaler usage is the most feasible and accessible strategy to increase inhaler effectiveness. Purpose To assess the impact of nurse-driven inhaler education on the compliance and proficiency of using inhalers among inhaler users. Methods This single-center, quasi-experimental study included the implementation of an individualized 60-min educational session on inhalers use. Health education and pretest and posttest outcomes were assessed by the Inhaler Proficiency Schedule and Patient Reported Behaviour tools. Results One hundred and twenty-one participants joined the study. At pretest, participants showed inadequate knowledge of general inhaler use. No previous training had been received by participants and difficulty with use and complications from using the inhalers were reported. At posttest, participants reported improvement in inhaler proficiency scores from 5.72 to 8.60 ( t = 17.99, df = 220, p < 0.001). Likewise, they showed a significant reduction towards the noncompliant behaviors from 15.21 to 11.19 ( t = 16.388, df = 238, p < 0.001). Conclusions Nurse-driven inhaler education yielded positive outcomes in both inhaler proficiency and compliance. The patients' assessment of using inhalers is crucial to determine the patients' educational deficits.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".