MétaCan
Menu
Back to cohort
Record W2581652780 · doi:10.1177/0844562116676119

Effectiveness of Nurse-Driven Inhaler Education on Inhaler Proficiency and Compliance Among Obstructive Lung Disease Patients: A Quasi-Experimental Study

2016· article· en· W2581652780 on OpenAlexvenueno aff
Mahmoud Al‐Kalaldeh, Mona Abd El-Rahman, Amal Baker Abo El-ata

Bibliographic record

VenueCanadian Journal of Nursing Research · 2016
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsInhalerMedicinePhysical therapySession (web analytics)NursingAsthmaInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.398
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicInhalation and Respiratory Drug DeliveryFrench-language works237,207