‘It's on my iPhone’: attitudes to the use of mobile computing devices in medical education, a mixed-methods study
Bibliographic record
Abstract
OBJECTIVE: The last decade has seen the introduction of new technology which has transformed many aspects of our culture, commerce, communication and education. This study examined how medical teachers and learners are using mobile computing devices such as the iPhone in medical education and practice, and how they envision them being used in the future. DESIGN: Semistructured interviews were conducted with medical students, residents and faculty to examine participants' attitudes about the current and future use of mobile computing devices in medical education and practice. A thematic approach was used to summarise ideas and concepts expressed, and to develop an online survey. A mixed methods approach was used to integrate qualitative and quantitative findings. SETTING AND PARTICIPANTS: Medical students, residents and faculty at a large Canadian medical school in 2011. RESULTS: Interviews were conducted with 18 participants (10 students, 7 residents and 1 faculty member). Only 213 participants responded to the online survey (76 students, 65 residents and 41 faculty members). Over 85% of participants reported using a mobile-computing device. The main uses described for mobile devices related to information management, communication and time management. Advantages identified were portability, flexibility, access to multimedia and the ability to look up information quickly. Challenges identified included: superficial learning, not understanding how to find good learning resources, distraction, inappropriate use and concerns about access and privacy. Both medical students and physicians expressed the view that the use of these devices in medical education and practice will increase in the future. CONCLUSIONS: This new technology offers the potential to enhance learning and patient care, but also has potential problems associated with its use. It is important for leadership in medical schools and healthcare organisations to set the agenda in this rapidly developing area to maximise the benefits of this powerful new technology while avoiding unintended consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".