Enhancing Pharmacy Student Learning and Perceptions of Medical Apps
Bibliographic record
Abstract
BACKGROUND: The use of mobile apps in health care is growing. Current and future practitioners must be equipped with the skills to navigate and utilize apps in patient care, yet few strategies exist for training health care professional students on the usage of apps. OBJECTIVE: To characterize first-year pharmacy student use of medical apps, evaluate first-year pharmacy student's perception of skills in finding, evaluating, and using medical apps before and after a focused learning experience, and assess student satisfaction and areas for improvement regarding the learning experience. METHODS: Students listened to a recorded, Web-based lecture on finding, evaluating, and using mobile apps in patient care. A 2-hour, interactive workshop was conducted during which students were led by an instructor through a discussion on strategies for finding and using apps in health care. The students practiced evaluating 6 different health care-related apps. Surveys were conducted before and after the focused learning experience to assess students' perceptions of medical apps and current use and perspectives on satisfaction with the learning experience and role of technology in health care. RESULTS: This educational intervention is the first described formal, interactive method to educate student pharmacists on medical apps. With a 99% response rate, surveys conducted before and after the learning experience displayed perceived improvement in student skills related to finding (52/119, 44% before vs 114/120, 95% after), evaluating (18/119, 15% before vs 112/120, 93% after), and using medical apps in patient care (31/119, 26% before vs 108/120, 90% after) and the health sciences classroom (38/119, 32% before vs 104/120, 87% after). Students described satisfaction with the educational experience and agreed that it should be repeated in subsequent years (89/120, 74% agreed or strongly agreed). Most students surveyed possessed portable electronic devices (107/119, 90% mobile phone) and agreed with the concept of medical apps being an important part of the health care profession in the future (112/119, 94% before and 115/120, 96% after). CONCLUSIONS: Student pharmacists recognize the key role technology plays in the future of health care. A medical apps workshop was successful in improving student pharmacists' perceptions of ability to find, evaluate, and use medical apps.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".