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Advances in development and evaluation of asthma education programs

2004· review· en· W2130623123 on OpenAlexaff
Shawna McGhan, Lisa Cicutto, A. Dean Befus

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2004
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of TorontoUniversity Health NetworkAstraZeneca (Canada)University of Alberta
Fundersnot available
KeywordsAsthmaMedicineAsthma managementProcess (computing)IncentivePatient educationMedical educationNursingComputer scienceImmunology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Effective asthma education requires more than merely providing information on asthma. Behavior change and learning principles must be incorporated into educational programs. However, there remains much debate and research about the most effective strategies to educate people to deal effectively with their asthma. This article focuses on recent advances in theoretical and practical strategies and examines core elements of successful asthma education programs. RECENT FINDINGS: Asthma education has improved in recent years as a result of application of evidence-based, theoretical principles that guide learning and behavior modification. Many studies show a refreshing focus on how to teach and have made substantial contributions to testing educational theories and making meaningful improvements to those with asthma. Successful asthma education programs include behavior change strategies, shared care practices and communication skills, a clear educational process, tailoring to client needs and influencing factors, multiple teaching formats, and a continuum of care. SUMMARY: An array of effective and innovative asthma education programs have been developed and tested. However, numerous areas in asthma education require improvement and further research, such as real-world models, sensitivities to underserved populations or venues, innovative partnerships, continuum of care, and patient incentive/participation.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.917
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.130
GPT teacher head0.460
Teacher spread0.330 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2004
Admission routes1
Has abstractyes

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