Educational impact of using smartphones for clinical communication on general medicine: More global, less local
Bibliographic record
Abstract
BACKGROUND: Medical trainees increasingly use smartphones in their clinical work. Similar to other information technology implementations, smartphone use can result in unintended consequences. This study aimed to examine the impact of smartphone use for clinical communication on medical trainees' educational experiences. DESIGN: Qualitative research methodology using interview data, ethnographic data, and analysis of e-mail messages. ANALYSIS: We analyzed the interview transcripts, ethnographic data, and e-mails by applying a conceptual framework consisting of 5 educational domains. RESULTS: Smartphone use increased connectedness and resulted in a high level of interruptions. These 2 factors impacted 3 discrete educational domains: supervision, teaching, and professionalism. Smartphone use increased connectedness to supervisors and may improve supervision, making it easier for supervisors to take over but can limit autonomy by reducing learner decision making. Teaching activities may be easier to coordinate, but smartphone use interrupted learners and reduced teaching effectiveness during these sessions. Finally, there may be professionalism issues in relation to how residents use smartphones during encounters with patients and health professionals and in teaching sessions. CONCLUSIONS: We summarized the impact of a rapidly emerging information technology-smartphones-on the educational experience of medical trainees. Smartphone use increase connectedness and allow trainees to be more globally available for patient care but creates interruptions that cause trainees to be less present in their local interactions with staff during teaching sessions. Educators should be aware of these findings and need to develop curriculum to address the negative impacts of smartphone use in the clinical training environment.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".