Residents’ use of mobile technologies: three challenges for graduate medical education
Bibliographic record
Abstract
Introduction: The practice of medicine involves, among other things, managing ambiguity, interpreting context and making decisions in the face of uncertainty. These uncertainties, amplified for learners, can be negotiated in a variety of ways; however, the promise, efficiency and availability of mobile technologies and clinical decision supports make these tools an appealing way to manage ambiguity.Mobile technologies are becoming increasingly prevalent in medical education and in the practice of medicine. Because of this, we explored how the use of mobile technologies is influencing residents' experiences of graduate medical education. Methods: We conducted an 18-month qualitative investigation to explore this issue. Our research was conceptually and theoretically framed in sociomaterial studies of professional learning. Specifically, our methods included logging of technology use and related reflexive writing by residents (n=10), interviews with residents (n=12) and interviews with faculty (n=6). Results: We identified three challenges for graduate medical education related to mobile technology use: (1) efficiency versus critical thinking; (2) patient context versus evidence-based medicine and (3) home/work-life balance. Discussion: In this digital age, decontextualised knowledge is readily available. Our data indicate that rather than access to accurate knowledge, the more pressing challenge for medical educators is managing how, when and why learners choose to access that information.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".