Awareness, Views, Perceptions, and Beliefs of Pharmacy Interns Regarding Digital Health in Saudi Arabia: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Digital health technologies and apps are rapidly advancing in recent years. It is expected to have more roles in transforming the health care system in this era of digital services. However, limited research is available regarding delivering digital health education in pharmacy and the pharmacy students' perspectives on digital health. OBJECTIVE: This study aims to assess pharmacy interns' awareness of digital health apps in Saudi Arabia and their views regarding the coverage of digital health in the education of pharmacists. In addition, we assessed the interns' perceptions and beliefs about the concepts, benefits, and implementation of digital health in practice settings. METHODS: A cross-sectional study using a web-based survey was conducted among pharmacy interns at Unaizah College of Pharmacy, Qassim University, Saudi Arabia. An invitation with a link to the web-based survey was sent to all interns registered at the college between January and March 2021. RESULTS: A total of 68 out of 77 interns registered in the internship year participated in this study, giving a response rate of 88%. The mean total score for pharmacy interns' awareness of digital health apps in Saudi Arabia was 5.66 (SD 1.74; maximum attainable score=7). The awareness with different apps ranged from 97% (66/68) for the Tawakkalna app to 65% (44/68) for the Ministry of Health 937 call center. The mean total score for attitude and beliefs toward concepts and benefits of telehealth and telemedicine apps was 58.25 (SD 10.44; maximum attainable score=75). In this regard, 84% (57/68) of the interns believed that telehealth could enhance the quality of care, 71% (48/68) believed that it could help effectively provide patient counseling, and 69% (47/68) believed it could improve patients' adherence to therapy. In this study, 41% (28/68) believed that the current coverage of digital health in the curriculum was average, whereas only 18% (12/68) believed it was high or very high coverage. Moreover, only 38% (26/68) attended additional educational activities related to digital health. Consequently, the majority (43/68, 63%) were of the opinion that there is a high or very high need to educate and train pharmacists in the field of digital health. CONCLUSIONS: Overall, the interns showed good awareness of common digital health apps in Saudi Arabia. Moreover, the majority of the interns had positive perceptions and beliefs about the concepts, benefits, and implementation of digital health. However, the findings showed that there is still scope for improvement in some areas. Moreover, most interns indicated that there is a need for more education and training in the field of digital health. Consequently, early exposure to content related to digital health and pharmacy informatics is an important step to help in the wide use of these technologies in the graduates' future careers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".