Factors influencing user decision of telemedicine applications in Thailand
Bibliographic record
Abstract
Telemedicine applications have been used worldwide to support the healthcare system in both private and government sectors. However, the factors influencing users' decisions to use the telemedicine application have not been well determined. Exploratory cross-sectional research was conducted using an offline and online questionnaire on Thai individuals aged 18-65. The recruitment period for this study spanned from December 25, 2023, to March 25, 2024, utilizing quota sampling to ensure representation across different regions of Thailand. The objectives were to estimate the proportion of individuals using telemedicine applications and to identify significant determinants of the decision to use telemedicine applications, including Acceptance and Use of Technology, the information systems (IS) Success Model, Trust, and Perceived Risk factors. Exploratory factor analysis (EFA) was used to identify potential latent factors from the 62-item multidimensional questionnaire. Multiple linear regression was used to identify significant determinants of using telemedicine applications. EFA was performed to group 62 variables into 6 latent factors, including trust, ease of use, system quality, benefits of use, price, and service quality. Of 385 Thai individuals, the proportion of those who use telemedicine applications was 63.63%. All six determinants significantly influenced the decision to use telemedicine applications. The factors influencing individuals' decisions to use telemedicine applications include trust, ease of use, system quality, benefits of use, price, and service quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".