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Record W2060080157 · doi:10.1097/tme.0b013e31818bf23d

Asthma Education Delivered in an Emergency Department and an Asthma Education Center

2009· article· en· W2060080157 on OpenAlexaff
Kim Szpiro, Margaret B. Harrison, Elizabeth G. VanDenKerkhof, M. Diane Lougheed

Bibliographic record

VenueAdvanced Emergency Nursing Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsPublic Health OntarioQueen's University
Fundersnot available
KeywordsMedicineAsthmaEmergency departmentIntervention (counseling)AnxietyPatient educationFamily medicinePhysical therapyNursingPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Implementation of asthma education in the emergency department (ED) setting is controversial. Time constraints and patient anxiety may be potential barriers to learning. This study aimed to describe the feasibility and impact of a brief evidence-based asthma education intervention delivered in the ED and an asthma education center (AEC) on asthma knowledge and perceived control. The educational intervention was easily integrated into routine care in the ED. Participants were similar except for age and current state of anxiety. At follow-up, both groups showed a significant increase in asthma knowledge (ED, n = 3.1, SD = 2.1, p < .01; AEC, n = 2.6, SD = 2.7, p = .01). Perceived control improved significantly in the ED (n = 2.6, SD = 3.9; p = .01). Despite higher state anxiety scores in the ED setting, a brief asthma educational intervention resulted in a short-term increase in asthma knowledge and perceived control. Asthma knowledge also increased after the intervention in the AEC setting. Educational initiatives are feasible and may be beneficial in an ED, providing an additional opportunity for counseling that should not be missed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.349
Teacher spread0.337 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2009
Admission routes1
Has abstractyes

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