A Review of Digital, Social, and Mobile Technologies in Health Professional Education
Bibliographic record
Abstract
INTRODUCTION: Digital, social, and mobile technologies (DSMTs) can support a wide range of self-directed learning activities, providing learners with diverse resources, information, and ways to network that support their learning needs. DSMTs are increasingly used to facilitate learning across the continuum of health professional education (HPE). Given the diverse characteristics of DSMTs and the formal, informal, and nonformal nature of health professional learning, a review of the literature on DSMTs and HPE could inform more effective adoption and usage by regulatory organizations, educators, and learners. METHODS: A scoping review of the literature was performed to explore the effectiveness and implications of adopting and using DSMTs across the educational continuum in HPE. A data extraction tool was used to review and analyze 125 peer-reviewed articles. Common themes were identified by thematic analysis. RESULTS: Most articles (56.0%) related to undergraduate education; 31.2% to continuing professional development, and 52.8% to graduate/postgraduate education. The main DSMTs described include mobile phones, apps, tablets, Facebook, Twitter, and YouTube. Approximately half of the articles (49.6%) reported evaluative outcomes at a satisfaction/reaction level; 45.6% were commentaries, reporting no evaluative outcomes. Most studies reporting evaluative outcomes suggest that learners across all levels are typically satisfied with the use of DSMTs in their learning. Thematic analysis revealed three main themes: use of DSMTs across the HPE continuum; key benefits and barriers; and best practices. DISCUSSION: Despite the positive commentary on the potential benefits and opportunities for enhancing teaching and learning in HPE with DSMTs, there is limited evidence at this time that demonstrates effectiveness of DSMTs at higher evaluative outcome levels. Further exploration of the learning benefits and effectiveness of DSMTs for teaching and learning in HPE is warranted.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".