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Record W2585211958 · doi:10.31372/20200503.1091

Kaʻu Community Asthma Management Program

2020· article· en· W2585211958 on OpenAlexvenueno aff
Luzviminda Banez Luzviminda Banez Miguel

Bibliographic record

VenueAsian/Pacific Island Nursing Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaPsychological interventionMedicineAsthma managementAction planSelf-managementFamily medicineGerontologyNursingInternal medicineManagement

Abstract

fetched live from OpenAlex

The “Kaʻu Community Asthma Management Program” (KCAMP) is a quality improvement and evidence-based practice Doctor of Nursing Practice (DNP) project. KCAMP’s objective was to determine whether community-based asthma education, self management, self-efficacy, an asthma action plan, journal writing, and use of peak flow meters reduce asthma exacerbations. The literature supports these effective interventions for asthma control (Andrews, Jones, & Mullan, 2014; Chen, Sheu, Chang, Wang, & Huang, 2010; Federman et al., 2013). KCAMP was designed with community-based interventions to improve the practice of management of asthma, decrease hospital and doctors’ visits’ costs, and improve the lives of people who have asthma. Fourteen adult residents with asthma from the Kaʻu District, ages 28 to 75, participated in the program. There were 64% (n = 9) females and 36% (n = 5) males. The racially diverse group included ten Hawaiians, three Asians, and one Caucasian.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.066
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0660.007

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 head0.088
GPT teacher head0.391
Teacher spread0.302 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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