Mobile app use by medical students and residents in the clinical setting: an exploratory study
Bibliographic record
Abstract
Introduction: Mobile devices and mobile applications facilitate access to clinical evidence at the point-of-care. Medical libraries play an important role in medical trainees' education, by subscribing to quality resources and by providing help and guidance on what apps to use. This study's goal was to explore medical trainees' mobile applications use in the clinical setting to help inform collection development's decisions and to provide insight on educational outreach. Perceived barriers and benefits of medical app use by clinical trainees was also explored. Methods: A brief online survey (English and French) was sent to all University of Ottawa clerkship medical students and residents. The questionnaire consisted of multiple choices, Likert-scale, and open-ended questions. Results: 208 English and 9 French responses were received. UpToDate was the most frequently used app, followed by MedCalc, Spectrum (CHEO) and Medscape. Respondents used medical apps mostly before and after meeting with patients and rarely while interacting with patients. Main benefits identified of medical app use were helping with decision-making, quick access to trustworthy clinical information, help with diagnosis and treatment options (e.g. medication dosage, drug interaction). Main barriers identified were costs, appearing unprofessional, lack of Canadian content and spotty hospital WiFi. Conclusion: Libraries' involvement in providing access to trustworthy clinical resources to medical trainees is important to help shape trainees' development as medical professionals. Outreach to learners in the clinical setting is crucial to educate on what apps are available to them through the library collection.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".