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Record W3214021712 · doi:10.1186/s12903-021-01957-7

Farsi version of the CLEFT-Q: translation, cultural adaptation process and reliability

2021· article· en· W3214021712 on OpenAlexfundno aff
Shabnam Ajami, Shiva Torabi, Samaneh Dehghanpour, Maryam Ajami

Bibliographic record

VenueBMC Oral Health · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersShiraz UniversityShiraz University of Medical SciencesMcMaster University
KeywordsReliability (semiconductor)MedicineAdaptation (eye)Set (abstract data type)Consistency (knowledge bases)Natural language processingMedical physicsLinguisticsComputer scienceArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was the translation and cultural adaptation of the CLEFT-Q to Farsi and evaluating the reliability of it. METHODS: The English version of the CLEFT-Q was translated to Farsi following the guidelines set forth by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). To calculate the reliability, 50 participants filled out the Farsi version of the questionnaire twice at 2-week intervals. RESULTS: The difficulties during the translation and cultural adaptation process were as follows: 7.56% of items from the independent forward translations, 62.18% of items from the comparison between two forward translations, and 21% of items from the comparison between post-back translation and the original version. The internal consistency and stability of the Farsi version of the CLEFT-Q were 0.979 and 0.997, which both were categorized as excellent. CONCLUSION: The Farsi version of the CLEFT-Q is a valid and reliable tool currently available for Farsi-speaking families around the world.

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)0.001
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 score0.032
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.414
GPT teacher head0.449
Teacher spread0.035 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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