Cross-cultural differences through subjective cognition: illustration in translatology with the SSTIC-E in the UAE
Bibliographic record
Abstract
The development of appropriate and valid multicultural and multilingual instruments research is necessary due to a growing multicultural and multilingual society in the 21st century. We explored the use of a cognitive scale related to subjective complaints, focusing on the first step: a cross-cultural and semantic validation. This study presents the translation and cross-validation process of the “Subjective Scale to Investigate Cognition in Schizophrenia” (SSTICS) for the United Arab Emirates (UAE) region via different languages used in Dubaï/Abu Dhabi. This scale measures cognitive complaints and has been validated with psychosis and used in 20 clinical trials worldwide. It evaluates areas of the illness related to self-awareness focusing on memory dysfunction and deficits of attention, language, and praxis. We described the method of cross-cultural validation, with back-translation, semantic steps, and societal contexts. The use of the Subjective Scale to Investigate Cognition in Emirates (SSTIC-E) was explored with different samples of UAE Arabic-speaking subjects. First, a pilot sample mean SSTICS total score was 16.5 (SD:16.9); (p < 0.001). The SSTIC-E was then administered to 126 patients and 84 healthy control participants. The healthy group has a lower mean score of 22.55 (SD = 12.04) vs. 34.06 (SD = 15.19). The method was extended to nine other languages, namely, Pakistani/Urdu, Hindi, Marathi, Lithuanian, Serbian, German, Romanian, Sinhala, and Russian. The scales are provided in the article. The overall aim of the translation process should be to stay close to the original version of the instrument so that it is meaningful and easily understood by the target language population. However, for construct validity, some items must be adapted at the time of translation to ensure that the questioned cognitive domain is respected. For example, cooking, an executive function, does not have the same occurrence for an Emirati male, or remembering a prime minister’s name, semantic memory, requires an electoral system to appoint the leader of a country. Translation methods and processes present many challenges but applying relevant and creative strategies to reduce errors is essential to achieve semantic validation. This study aims to measure personally experienced knowledge or attitudes; such language effects can be a thorny problem.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".