Considerations for the Telehealth Systems of Tomorrow: An Analysis of Student Perceptions of Telehealth Technologies
Bibliographic record
Abstract
BACKGROUND: While much is known about factors that facilitate telehealth adoption, less is known about why adoption does or does not occur in specific populations, such as students. OBJECTIVE: This study aims to examine the perceptions of telehealth systems within a large student sample. METHODS: Undergraduate students (N=315) participated in a survey of the perceived advantages and disadvantages of telehealth technologies. The responses to the survey were analyzed using thematic analysis. RESULTS: We found that students were likely to adopt telehealth systems for the following reasons: (1) the system worked efficiently, (2) the convenience of telehealth, and (3) to gain access to health services. Students also perceived several disadvantages to telehealth systems, such as issues of trust (ie, security, privacy), the impersonal nature of telehealth systems, and they were concerned about the potential for major system errors. CONCLUSION: By understanding the current barriers to telehealth adoption in a cohort of students, we can not only better anticipate the future needs of this group, but also incorporate such needs into the design of future telehealth systems.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".