Examining and establishing translational and conceptual equivalence of survey questionnaires for a multi-ethnic, multi-language study
Bibliographic record
Abstract
AIM: This paper is a report of techniques used to examine and establish translational and conceptual equivalence of survey questionnaires. BACKGROUND: A major concern arose about standardization of translated survey questionnaires, when preparing to evaluate differences in acute coronary syndrome presentation in European (White), Chinese and South Asian patients. METHODS: The survey questionnaires were first translated by an accredited translation company. Between July and November 2009, materials were taken to like-speaking healthcare reviewers to ensure that the clinical meaning was appropriate. Like-speaking lay reviewers were then asked to make comment about grammar; meaning and understanding of questions; and any concerns about the suitability of graphics. A key informant from each language group reviewed all comments and worked with the investigators and the translation company to create final sets of survey questionnaires. RESULTS: Readability of the questionnaires (too complex or too basic) was the most common concern. A major discrepancy between ethnic groups arose about a graphic of 'squeezing' pain. A hand grasping a balloon was considered appropriate for European and South Asian groups, while a picture of a towel being wrung out was identified as more appropriate for the Chinese. There were no negative comments about the graphics. Soliciting key informants who were highly fluent in both English and the language under study was critical to ensure that the participants' feedback was appropriately reconciled. CONCLUSION: Traditional forward-backward translation of study materials is insufficient. Translation must be accompanied by a process whereby equivalence and acceptability are also established.
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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 | 0.533 | 0.665 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".