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Record W582017209 · doi:10.7275/7201712

Asthma Education and Care Coordination in the Medical Home

2024· article· en· W582017209 on OpenAlexaboutno aff
Veronica G Mansfield

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2024
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAsthmaSpecialtyPatient educationNursingHealth careFamily medicineMedical homeEmergency departmentIntervention (counseling)Primary care

Abstract

fetched live from OpenAlex

Asthma is the most common chronic disease in childhood. In spite of the many advances in the medical management of asthma, families frequently seek treatment for acute exacerbations of asthma in the emergency department. The purpose of this project was to evaluate asthma care provided to pediatric patients at a community health center in New England through scheduled nursing visits for asthma education and care coordination. The asthma education and care coordination program was developed based on both the NHLBI, NAEPP, Expert Panel 3, 2007 guidelines and ANA White Paper and Agency for Health Care Quality (AHRQ) value of nursing care coordination. The evaluation phase of the Ottawa Model for Research Utilization Model using data gathered from patients, practitioners, and systems was used to assess the effectiveness of the intervention. Results of this evaluation reveal that although all of the nurses received education and training at each of twelve clinic sites the results from the three sites identify that the nurses were not providing the asthma education and care coordination visits. Recommendations include further education, training, and a change in clinic nurse role to fully implement the program.

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)0.006
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.251
Teacher spread0.243 · 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
Published2024
Admission routes1
Has abstractyes

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