Left to their own devices: Medical learners’ use of mobile technologies
Bibliographic record
Abstract
BACKGROUND: Although many medical learners and teachers are using mobile technologies within medical education, there has been little evidence presented describing how they use mobile devices across a whole curriculum. METHODS: The Northern Ontario School of Medicine (NOSM) introduced a new mobile device program in 2010. Incoming undergraduate medical learners received a laptop and an iPad and learners entering year three of the four-year program received a laptop and an iPhone. A survey was sent to all learners to gather information on their use of and attitudes toward these devices. A combination of quantitative and qualitative methods was used to analyze the data and to generate a series of themes that synthesized student behaviors, perceptions and attitudes. RESULTS: Context and learner autonomy were found to be important factors with learners using multiple devices for different purposes and adopting strategic approaches to learning using these devices. The expectation that school-issued devices would be regularly and enthusiastically used to replace more traditional study media was not reflected in practice. CONCLUSIONS: Learners' approaches to using mobile devices are heterogeneous as is the extent to which they use them. Learners adapt their use of mobile devices to the learning cultures and contexts they find themselves in.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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 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".