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Record W2986471073 · doi:10.2196/14189

Usability of the Turkish Translation of the Dutch Talking Touch Screen Questionnaire for Physical Therapy Patients With a Turkish Background: Qualitative Study

2019· article· en· W2986471073 on OpenAlexvenueno aff
Marlies Welbie, Harriët Wittink, Sahin Bozkurt, Tugba Coban, W.L.J.M. Devillé

Bibliographic record

VenueJMIR Formative Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityTurkishHealth literacyThematic analysisApplied psychologyQualitative researchPsychologyMedicineMedical educationHealth carePhysical therapyComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: The Turkish translation of the Dutch Talking Touch Screen Questionnaire (TTSQ) has been developed to help physical therapy patients with a Turkish background in the Netherlands to autonomously elucidate their health problems and impairments and set treatment goals, regardless of their level of health literacy. OBJECTIVE: The aim of this study was to evaluate the usability of the Turkish TTSQ for physical therapy patients with a Turkish background with diverse levels of health literacy and experience in using mobile technology. METHODS: The qualitative Three-Step Test-Interview method was carried out to gain insight into the usability of the Turkish TTSQ. A total of 10 physical therapy patients participated. The interview data were analyzed using a thematic content analysis approach aimed at determining the accuracy and completeness with which participants completed the questionnaire (effectiveness), the time it took participants to complete the questionnaire (efficiency), and the extent to which the participants were satisfied with the ease of use of the questionnaire (satisfaction). The problems encountered by the participants in this study were given a severity rating, which was used to provide a rough estimate of the need for additional usability improvements. RESULTS: No participant in this study was able to complete the questionnaire without encountering at least one usability problem. A total of 17 different kinds of problems were found. On the basis of their severity score, 3 problems that should be addressed during future development of the tool were "Not using the navigation function of the photo gallery in Question 4 causing the participant to not see all presented response items;" "Touching the text underneath a photo in Question 4 to select an activity instead of touching the photo itself, causing the activity not to be selected;" and "Pushing too hard or tapping too softly on the touch screen causing the touch screen to not respond." The data on efficiency within this study were not valid and are, therefore, not reported in this study. No participant was completely satisfied or dissatisfied with the overall ease of use of the Turkish TTSQ. Two participants with no prior experience of using tablet computers felt that, regardless of what kinds of improvement might be made, it would just be too difficult for them to learn to work with the device. CONCLUSIONS: As with the Dutch TTSQ, the Turkish TTSQ needs improvement before it can be released. The results of this study confirm the conclusion of the Dutch TTSQ study that participants with low levels of education and little experience in using mobile technology are less able to operate the TTSQ effectively. Using a Dutch speaking interviewer and Turkish interpreter has had a negative effect on data collection in this study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.117
GPT teacher head0.537
Teacher spread0.420 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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