Digital Learning Tools in Postgraduate Family Medicine Training: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Medical education is undergoing a digital revolution, yet few studies have examined digital learning tools in postgraduate family medicine training. This scoping review aims to identify existing tools, describe their use, and suggest future research directions. METHODS: We conducted a search of six academic databases and gray literature in 2021 and updated it in 2022. We mapped full-text English or French publications from 2010 onward that featured digital learning tools for family medicine trainees based on tool types, learning outcomes, core competencies, and educational outcomes. RESULTS: Out of 2058 records, 39 studies met inclusion criteria. Twenty-six studies (66.6%) described online computer-based tools. Simulations, including virtual and augmented reality, were featured in seven studies (17.9%) and mobile applications in three studies (7.7%). The tools were designed to facilitate skills and knowledge in examination and procedures (36%), pathology (33%), pharmacology (23%), and communication (18%). The majority targeted competencies relevant to the practice of primary care in the community or office setting (82%), with fewer addressing maternal care (8%) and scholarship (8%). None addressed home/long-term care, hospital care, or leadership/advocacy. In terms of educational outcomes, most studies assessed knowledge/skills (72%), learning experience (59%), and attitudes (46%), while few evaluated behavioral change (5%), organizational impact (3%), or patient care (0%). CONCLUSIONS: We identified very few articles on digital learning tools in postgraduate family medicine education. Our findings reveal critical gaps, including limited integration of innovative technologies, unaddressed core competencies, and insufficient evaluation of the outcomes of digital learning tools.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".