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Mapping the literature on the qualitative approach to childhood asthma from 1996 to 2018: a bibliometric analysis

2024· article· en· W4392695905 on OpenAlexaboutno aff
Cristina Torres-Pascual, Emily Granadillo, Adriana Romero-Sandoval, Alejandro Rodríguez, Philip J. Cooper, Natalia Romero-Sandoval

Bibliographic record

VenueRevista Brasileira de Saúde Materno Infantil · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaBibliometricsData scienceComputer scienceLibrary scienceMedicine

Abstract

fetched live from OpenAlex

Resumo Objectives: to describe the scientific production of qualitative studies in childhood asthma. Methods: bibliometric analysis. Articles were from Web of Science, Scopus, Cochrane, and PubMed (1996-2018), using the search terms asthma, children, qualitative research, qualitative study, qualitative analysis, ethnographic, phenomenology and narrative. Results: 258 articles were retrieved from 143 journals, representing 1.2% of scientific articles on childhood asthma. The growth rate was high. Authorship included 969 authors (85.3% occasional) from 279 institutions. 94.2% were co-authored and 3.5% were international collaborations. The greatest number of articles were from the United States (45.3%), United Kingdom (17.4%) and Canada (7.4%). The categories with the highest number of articles were Nursing & Public, Environmental & Occupational Health (18.2%), Respiratory System (10.1%) and Allergy (7.7%). 99.7% of the articles were in English. Conclusion: these results show a lack of consolidation of the literature based on qualitative studies on childhood asthma with a high percentage of occasional authors and limited international collaboration, indicating a need to strengthen this approach.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Scholarly communication
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0210.168
Science and technology studies0.0010.000
Scholarly communication0.0050.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.095
GPT teacher head0.419
Teacher spread0.324 · 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; both teacher heads agree on what is shown here.

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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