Cross-Cultural Adaptation of Executive Function Performance Test (EFPT): ACOSMIN Systematic Review
Bibliographic record
Abstract
Background: Psychometrics plays a crucial role in cross-cultural research, necessitating the adaptation of scales for measuring health status. As the Executive Function Performance Test measures functional cognition and can be helpful in medical, rehabilitation, and research settings, this review aims to methodologically review cross-culturally adapted versions of the Executive Function Performance Test using the COSMIN checklist. Methods: The present systematic review was conducted based on the COSMIN methodology. After searching PubMed, Scopus, Google Scholar, and Web of Science with keywords ranging from July 2024, seven articles were selected for a thorough methodological review matching the review objectives. Results: The outcomes revealed that cultural adaptation has similar phases with a few differences. The most frequently reported forms of reliability were interrater reliability and internal consistency, while some variations in validity assessment were found. Conclusion: Proper cross-cultural adaptation requires steps of translation, cultural adaptation, and assessing validity and reliability. Since the tool does not provide adequate psychometric data, using it in the clinical field and research would be questionable. To adapt the Executive Function Performance Test to different cultures and languages, and also to measure its validity and reliability across various diseases that have not been previously assessed, further research is needed, following the steps outlined in this review.
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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 | 0.022 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".