Teaching inhaler use in chronic obstructive pulmonary disease patients
Bibliographic record
Abstract
PURPOSE: To review barriers to the successful use of inhalers in patients with chronic obstructive pulmonary disease (COPD), and the role of the nurse practitioner (NP) in facilitating optimum inhaler use. DATA SOURCES: Review of the national and international scientific literature. CONCLUSIONS: Pharmacologic treatment of COPD patients comprises mainly inhaled medications. Incorrect use of inhalers is very common in these individuals. Some of the consequences of poor inhaler technique include reduced therapeutic dosing, medication adherence, and disease stability, which can lead to increased morbidity, decreased quality of life, and a high burden on the healthcare system. Knowledgeable evaluation and frequent reassessment of inhaler use coupled with education of patients, caregivers, and healthcare professionals can significantly improve the benefits COPD patients derive from inhaled therapy. IMPLICATIONS FOR PRACTICE: Patient education is vital for correct use of inhalers and to ensure the effectiveness of inhaled medications. The NP has a critical role in assessing potential barriers to successful learning by the patient and improving inhaler technique and medication management. The NP can also facilitate success with inhaled medications by providing up-to-date inhaler education for other healthcare team members, who may then act as patient educators.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".