Knowledge, attitudes and perception of medical and nursing students towards telemedicine/telehealth
Bibliographic record
Abstract
Introduction: Despite widespread use of advanced technology in a range of health applications, telemedicine is still in its infancy. Acceptance of telehealth/telemedicine strategies in health-care has increased significantly, in part due to the COVID-19 pandemic. Previous studies indicate a significant gap in preparation of healthcare providers in e-medicine concepts, despite some exposure to telemedicine during training. The purpose of this study was to explore knowledge, attitudes, and perception to gauge the readiness of medical and nursing students to engage in telemedicine. Methods: Using a cross-sectional research design, a 26-item questionnaire was administered electronically to nursing and medical students attending institutions in the Southern United States. Results: A total of 109 students completed the survey. The mean age of participants was 28.28 (SD=8.46). The majority of participants were nursing students (61.5%), female (82.6%), and white (74.3%). With regard to knowledge, only 23% feel the curriculum adequately prepared them for telemedicine/telehealth. Sixty percent of respondents said they agreed or strongly agreed that telemedicine lowers healthcare expenses, while 40% said it improves healthcare quality. Nearly a quarter (24%) reported that they are very/completely likely to use telemedicine in practice after graduation. Perceived obstacles in practicing telemedicine included technology that is difficult to use (31.2%), disinterest among clients (25.7%), and lack of adequate telemedicine training (20.2%). Discussion: This study demonstrated that health-care students have a perception that they are inadequately prepared for the challenges of telemedicine/telehealth, despite recognition of its potential value. Given the significant increase in the use of telemedicine/telehealth, additional studies are needed to design a more effective health-care curriculum to ensure proper preparation and instill confidence in the next generation of health-care providers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".