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Record W2606428817 · doi:10.2147/jaa.s133481

A framework for measuring self-management effectiveness and health care use among pediatric asthma patients and families

2017· review· en· W2606428817 on OpenAlexfundno aff
Pavani Rangachari

Bibliographic record

VenueJournal of Asthma and Allergy · 2017
Typereview
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
FundersInstitute of Population and Public HealthAugusta University
KeywordsMedicineAsthmaSelf-managementFamily medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Asthma is associated with substantial health care expenditures, including an estimated US$56 billion per year in direct costs. A recurring theme in the asthma management literature is that costly asthma symptoms, including hospitalizations and multiple emergency department (ED)/outpatient visits, can often be prevented through patient/family adherence to the national (National Institutes of Health Expert Panel Report-3) guidelines for effective self-management of asthma, specifically 1) medication adherence and 2) environmental trigger avoidance, as outlined in the patient's personalized Asthma-Action Plan. It is important to note however that while effective self-management of asthma is known to reduce ED visits and hospitalizations, the relationship between asthma self-management effectiveness and outpatient visit frequency remains ambiguous, reflecting a gap in the literature. For instance, do patients/families who self-manage effectively visit outpatient clinics more frequently for asthma care (compared to those who do not self-manage effectively), after accounting for differences in asthma severity, demographic characteristics, and risk factors? Do patients/families who visit outpatient clinics more frequently for asthma care, in turn have fewer ED and inpatient encounters for asthma? On the other hand, do patients/families who do not revisit outpatient clinics regularly have higher ED visits and hospitalizations? It is important to address these gaps, in order to reduce the costs and public health burden of asthma. This paper provides a foundation for addressing these gaps, by conducting an integrative review of the asthma management literature, to develop a conceptual framework for measuring self-management effectiveness and health care use among pediatric asthma patients/families. In doing so, the paper lays the groundwork for future research seeking to explicate the relationship between asthma self-management effectiveness and health care use, which in turn has potential to engage asthma providers in promoting ideal self-management and optimal health care use for pediatric asthma, in accordance with national evidence-based guidelines for asthma management.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.129
GPT teacher head0.442
Teacher spread0.313 · 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.

Study designNot applicable
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

Citations10
Published2017
Admission routes1
Has abstractyes

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