Translation method is validity evidence for construct equivalence: analysis of secondary data routinely collected during translations of the Health Literacy Questionnaire (HLQ)
Bibliographic record
Abstract
Abstract Background Cross-cultural research with patient-reported outcomes measures (PROMs) assumes that the PROM in the target language will measure the same construct in the same way as the PROM in the source language. Yet translation methods are rarely used to qualitatively maximise construct equivalence or to describe the intents of each item to support common understanding within translation teams. This study aimed to systematically investigate the utility of the Translation Integrity Procedure (TIP), in particular the use of item intent descriptions, to maximise construct equivalence during the translation process, and to demonstrate how documented data from the TIP contributes evidence to a validity argument for construct equivalence between translated and source language PROMs. Methods Analysis of secondary data was conducted on routinely collected data in TIP Management Grids of translations (n = 9) of the Health Literacy Questionnaire (HLQ) that took place between August 2014 and August 2015: Arabic, Czech, French (Canada), French (France), Hindi, Indonesian, Slovak, Somali and Spanish (Argentina). Two researchers initially independently deductively coded the data to nine common types of translation errors. Round two of coding included an identified 10th code. Coded data were compared for discrepancies, and checked when needed with a third researcher for final code allocation. Results Across the nine translations, 259 changes were made to provisional forward translations and were coded into 10 types of errors. Most frequently coded errors were Complex word or phrase (n = 99), Semantic (n = 54) and Grammar (n = 27). Errors coded least frequently were Cultural errors (n = 7) and Printed errors (n = 5). Conclusions To advance PROM validation practice, this study investigated a documented translation method that includes the careful specification of descriptions of item intents. Assumptions that translated PROMs have construct equivalence between linguistic contexts can be incorrect due to errors in translation. Of particular concern was the use of high level complex words by translators, which, if undetected, could cause flawed interpretation of data from people with low literacy. Item intent descriptions can support translations to maximise construct equivalence, and documented translation data can contribute evidence to justify score interpretation and use of translated PROMS in new linguistic contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 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 teacher head, 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".