A Pilot Study of Adapting and Assessing an Online Aphasia Therapy Software for Turkish Speakers: The Influence of Variables on Naming Accuracy
Bibliographic record
Abstract
BACKGROUND: The adaptation of aphasia therapy tools into different languages and cultural contexts is essential for broadening access to effective rehabilitation resources. Currently, most online aphasia therapy software is available primarily in English with limited resources for Turkish-speaking individuals. AIMS: This study aimed to address this gap by translating and culturally adapting an English-language online aphasia therapy software into Turkish, focusing on tasks related to auditory comprehension, reading, writing and naming. The study also evaluated the performance of ten healthy individuals aged 40 to 60 on these adapted tasks and explored the impact of factors such as word frequency, age, education level, gender and cognitive ability on task performance. METHODS AND PROCEDURE: The software was adapted according to the World Health Organisation (WHO) and Seçer's (2018) adaptation guidelines and tasks were administered to ten healthy participants. Participant eligibility was determined independently using the Montreal Cognitive Assessment-Turkish Version (MoCA-TR) and the Beck Depression Inventory (BDI). In addition to serving as an inclusion criterion, MoCA-TR scores were also included as a variable in the analysis. Additionally, feedback was collected from 17 speech-language therapists regarding the software's usability. All statistical analyses were conducted using IBM SPSS Statistics. OUTCOMES AND RESULTS: = 0.729, p = 0.002). In contrast, gender had no significant effect on task performance. Overall, participants achieved a mean accuracy of 92.7% on easy, 88.3% on moderate and 85.9% on difficult tasks, completing the task in an average of 56.6 minutes. Feedback from 17 SLTs was predominantly positive, with 82.4% reporting ease of access and 94.1% indicating willingness to use or recommend the software in clinical settings. CONCLUSION: This pilot study of the Turkish adaptation of this online aphasia therapy software holds promise for enhancing aphasia therapy and addresses the need for more Turkish-language resources in online language therapy. The marginally lower accuracy on more difficult tasks and its correlation with education and cognitive performance suggest the software offers an appropriate range of challenge across aphasia types and severities while avoiding ceiling effects. WHAT THIS PAPER ADDS: What is already known on this subject Digital aphasia therapy tools exist in various languages, providing structured activities to support individuals with aphasia. The effectiveness of these tools depends on linguistic and cultural appropriateness which influences their usability and accuracy in therapy. Word frequency, education level and cognitive abilities impact naming accuracy in both healthy individuals and those with aphasia. Existing studies suggest that high-frequency words are recalled more easily while factors such as socioeconomic background and cognitive reserve influence language performance. What this paper adds to the existing knowledge This study adapted and culturally customised Aphasia Therapy Online for Turkish speakers, ensuring its suitability for the linguistic and cultural characteristics of the population. A pilot study with ten healthy individuals demonstrated that accuracy rates varied based on word frequency, confirming that high-frequency words were named more easily than medium- and low-frequency words. Alternative culturally appropriate words were integrated into the software, improving its applicability for Turkish users. A survey of speech and language therapists highlighted the software's usability and potential for home practice, supporting its integration into clinical workflows. It is also the first paper related to the software. What are the potential or actual clinical implications for this work? The findings emphasise the importance of culturally adapting digital therapy tools to improve their effectiveness in diverse populations. Turkish speech and language therapists can use Aphasia Therapy Online as a complementary tool to extend therapy dosage and enhance engagement. The integration of alternative culturally relevant words and real photographs ensures that the software is more accessible and effective for Turkish speakers with aphasia. Given the significant impact of education level on task performance, customising therapy tasks based on cognitive reserve and linguistic background is recommended for optimal outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".