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Translation and cross-cultural adaptation of the mixed methods appraisal tool to the brazilian context

2020· article· en· W3041927334 on OpenAlexaff
Rafaella Queiroga Souto, Karina Sotero de Araújo Lima, Pierre Pluye, Quan Nha Hong, Kimberly Barbosa, Gleicy Karine Nascimento de Araújo-Monteiro

Bibliographic record

VenueRevista de Pesquisa Cuidado é Fundamental Online · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsYork UniversityMcGill University
Fundersnot available
KeywordsAdaptation (eye)CLARITYContext (archaeology)Computer scienceQuality (philosophy)Process (computing)PsychologyEpistemologyGeography

Abstract

fetched live from OpenAlex

Objective: Transculturally translate and adapt the Mixed Methods Appraisal Tool to the Brazilian reality. Methods: The methodology followed the 10 steps determined by the transcultural translation and adaptation process conducted by one of the Working Groups of the Special Interest Group on Quality of Life and the Cultural Translation and Adaptation Group. Results: o The test with the instrument was carried out from the development of two systematic systematic reviews. The translated version obtained a strong / substantial Kappa coefficient (k = 0.67), and was titled "Method of evaluating the quality of researches with mixed methods - Version 2011". The produced version presents structural and semantic components compatible with those of the original version, allowing good understanding and brings clarity in its content. Conclusion: the translated and adapted instrument can be an important tool for scientific production in Brazil, optimizing the production of systematic reviews in the different areas of knowledge.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.315
GPT teacher head0.490
Teacher spread0.175 · 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 designNot applicable
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

Citations4
Published2020
Admission routes1
Has abstractyes

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