Residents’ and preceptors’ perceptions of the use of the iPad for clinical teaching in a family medicine residency program
Bibliographic record
Abstract
BACKGROUND: As Family Medicine programs across Canada are transitioning into a competency-based curriculum, medical students and clinical teachers are increasingly incorporating tablet computers in their work and educational activities. The purpose of this pilot study was to identify how preceptors and residents use tablet computers to implement and adopt a new family medicine curriculum and to evaluate how they access applications (apps) through their tablet in an effort to support and enhance effective teaching and learning. METHODS: Residents and preceptors (n = 25) from the Family Medicine program working at the Pembroke Regional Hospital in Ontario, Canada, were given iPads and training on how to use the device in clinical teaching and learning activities and how to access the online curriculum. Data regarding the use and perceived contribution of the iPads were collected through surveys and focus groups. This mixed methods research used analysis of survey responses to support the selection of questions for focus groups. RESULTS: Reported results were categorized into: curriculum and assessment; ease of use; portability; apps and resources; and perceptions about the use of the iPad in teaching/learning setting. Most participants agreed on the importance of accessing curriculum resources through the iPad but recognized that these required enhancements to facilitate use. The iPad was considered to be more useful for activities involving output of information than for input. Participants' responses regarding the ease of use of mobile technology were heterogeneous due to the diversity of computer proficiency across users. Residents had a slightly more favorable opinion regarding the iPad's contribution to teaching/learning compared to preceptors. CONCLUSIONS: iPad's interface should be fully enhanced to allow easy access to online curriculum and its built-in resources. The differences in computer proficiency level among users should be reduced by sharing knowledge through workshops led by more skillful iPad users. To facilitate collection of information through the iPad, the design of electronic data-input forms should consider the participants' reported negative perceptions towards typing data through mobile devices. Technology deployment projects should gather sufficient evidence from pilot studies in order to guide efforts to adapt resources and infrastructure to relevant needs of Family Medicine teachers and learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".