Using the Technology Acceptance Model to Characterize Barriers and Opportunities of Telemedicine in Rural Populations: Survey and Interview Study
Bibliographic record
Abstract
Background Health care access issues have long plagued rural Americans. One approach to alleviating the challenges and poor health outcomes for rural individuals is through the use of telemedicine, sometimes called telehealth. It is important to understand factors that may be related to telemedicine adoption or nonadoption, particularly in underserved rural settings. Objective This pilot study examines telemedicine perceptions among rural, underserved populations using the Technology Acceptance Model, which serves as a framework to explore the adoption of telemedicine services by those who have used it. This study also explores the differences between user and nonuser perceptions of telemedicine. Methods Paper surveys and phone interviews were conducted in rural Northern Lower Michigan. Results Perceived usefulness and perceived ease of use explained 91% of the variability in attitude toward telemedicine (R2=0.91; F1,15=73.406; P<.001). Ease of use was a significant predictor (mean 2.36, SD 1.20; P<.001), but usefulness (mean 3.16, SD 0.81; P=.20) was not. Furthermore, there were significant differences in individual perception of telemedicine between users and nonusers. For example, nonusers believed they would receive better care in person (users: mean 3.30, SD 1.22; nonusers: mean 1.91, SD 1.14; F1,32=10.126; P=.003). The quantitative findings were reinforced by the qualitative results from the phone interviews. Conclusions Overall, the Technology Acceptance Model is an appropriate model to understand the attitudes toward telemedicine that may lead to its adoption by rural Americans.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".