Neuropsychological application of the International Test Commission Guidelines for Translation and Adapting of Tests
Bibliographic record
Abstract
OBJECTIVE: The number of test translations and adaptations has risen exponentially over the last two decades, and these processes are now becoming a common practice. The International Test Commission (ITC) Guidelines for Translating and Adapting Tests (Second Edition, 2017) offer principles and practices to ensure the quality of translated and adapted tests. However, they are not specific to the cognitive processes examined with clinical neuropsychological measures. The aim of this publication is to provide a specialized set of recommendations for guiding neuropsychological test translation and adaptation procedures. METHODS: The International Neuropsychological Society's Cultural Neuropsychology Special Interest Group established a working group tasked with extending the ITC guidelines to offer specialized recommendations for translating/adapting neuropsychological tests. The neuropsychological application of the ITC guidelines was formulated by authors representing over ten nations, drawing upon literature concerning neuropsychological test translation, adaptation, and development, as well as their own expertise and consulting colleagues experienced in this field. RESULTS: A summary of neuropsychological-specific commentary regarding the ITC test translation and adaptation guidelines is presented. Additionally, examples of applying these recommendations across a broad range of criteria are provided to aid test developers in attaining valid and reliable outcomes. CONCLUSIONS: Establishing specific neuropsychological test translation and adaptation guidelines is critical to ensure that such processes produce reliable and valid psychometric measures. Given the rapid global growth experienced in neuropsychology over the last two decades, the recommendations may assist researchers and practitioners in carrying out such endeavors.
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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 | 0.205 | 0.510 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".