Seeking coherence between ‘mobile learning’ applications and the everyday lives of medical residents
Bibliographic record
Abstract
INTRODUCTION: The role of technology in health professions education has received increased research attention. Research has examined the interaction between humans and technology, focusing on the mutual influence between people and technology. Little attention has been given to the role of motivation and incentives in how learning technologies are used in relation to daily activities. This research aims to understand the relationship between medical-learning technology and its users. METHODS: A mixed-method case study of a new medical-learning mobile application (app) for family medicine residents was undertaken at a Canadian university hospital. The Information Assessment Method is a custom-made app to help residents prepare for the College of Family Physicians of Canada licensing examination. Residents' use of the app was tracked over a 7-month period and individual, semi-structured interviews were conducted with users. Data were thematically analyzed and correlated with app use data. RESULTS: Factors identified as shaping residents' mobile app use for learning, included: efficiency, mobility and resonance with life context; credibility of information retrieved; and relevance of content. Most influential was stage of residency. Second-year residents were more selective and strategic than first-year residents in their app use. DISCUSSION: An emphasis on coherence between self-directed learning and externally dictated learning provides a framework for understanding the relationship between users and mobile-learning technology. This framework can guide the design, implementation and evaluation of learning interventions for healthcare professionals and learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".