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Record W2803730338 · doi:10.1055/s-0038-1655750

Translation and Cultural Adaptation of the Short-Form Food Frequency Questionnaire for Pregnancy into Brazilian Portuguese

2018· article· en· W2803730338 on OpenAlexaff
Karina Tamy Kasawara, Daiane Sofia Morais Paulino, Roberta Bgeginski, Chistine L. Cleghorn, Michelle F. Mottola, Fernanda Garanhani Surita

Bibliographic record

VenueRevista Brasileira Ginecologia e Obstetrícia · 2018
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsDebriefingPortugueseBrazilian PortugueseAdaptation (eye)PsychologyMedicineSocial psychologyLinguistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To translate and culturally adapt the short-form Food Frequency Questionnaire (SFFFQ) for pregnant women, which contains 24 questions, into Brazilian Portuguese. METHODS: Description of the process of translation and cultural adaptation of the SFFFQ into Brazilian Portuguese. The present study followed the recommendation of the International Society for Pharmacoeconomics and Outcomes Research for translation and cultural adaptation with the following steps: 1) preparation; 2) first translation; 3) reconciliation; 4) back translation; 5) revision of back translation; 6) harmonization; 7) cognitive debriefing; 8) revision of debriefing results; 9) syntax and orthographic revision; and 10) final report. Five obstetricians, five dietitians and five pregnant women were interviewed to contribute with the language content of the SFFFQ. RESULTS: Few changes were made to the SFFFQ compared with the original version. These changes were discussed with the research team, and differences in language were adapted to suit all regions of Brazil. CONCLUSION: The SFFFQ translated to Brazilian Portuguese can now be validated for use in the Brazilian population.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.215
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.044
GPT teacher head0.302
Teacher spread0.258 · 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 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
Published2018
Admission routes1
Has abstractyes

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