Evaluation of Cross-Cultural Adaptation and Measurement Properties of STarT Back Screening Tool: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this systematic review was to investigate the extent to which the STarT Back Screening Tool (SBST) has been evaluated for (1) the quality of translation of evidence for cross-cultural adaptation and (2) the measurement properties in languages other than English. METHODS: A systematic search of 8 databases, including Medline, Embase, CINAHL, PsycINFO, AMED, Scopus, PubMed, and Web of Science, was performed. Electronic databases were searched for the period between 2008 and December 27, 2016. We included studies related to cross-cultural adaptation, including translation and assessment of the measurement properties of SBST. Study selection, translation, methodologic and quality assessments, and data extraction were performed independently by 2 reviewers. RESULTS: Of the 1566 citations retrieved, 17 studies were admissible, representing 11 different SBST versions in 10 languages. The quadratic weighted κ statistics of the 2 reviewers, for the translation, methodologic assessment, and quality assessment were 0.85, 0.76, and 0.83, respectively. For translation, only 2 versions (Belgian-French and Mandarin) fulfilled all requirements. None of the versions had tested all the measurement properties, and when performed, these were found to have been conducted inadequately. With regard to quality assessment, overall, the included versions had a "Poor" total summary score except 2 (Persian and Swiss-German), which were rated as "Fair." CONCLUSIONS: Few versions fully met the standard criteria for valid translation, and none of the versions tested all the measurement properties. There is a clear need for more accurate cross-cultural adaptation of SBST and greater attention to the quality of psychometric evaluation of the adapted versions of SBST. At this time, caution is recommended when using SBST in languages other than English.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.112 | 0.341 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".