Insights for Teaching During a Pandemic: Lessons From a Pre-COVID-19 International Synchronous Hybrid Learning Experience
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Medical educators and researchers have increasingly sought to embed online educational modalities into graduate medical education, albeit with limited empirical evidence of how trainees perceive the value and experience of online learning in this context. The purpose of this study was to explore the experiences of hybrid learning in a graduate research methods course in a family medicine and primary care research graduate program. METHODS: This qualitative description study recruited 28 graduate students during the fall 2016 academic term. Data sources included qualitative group discussions and a 76-item online survey collected between March and September 2017. We used thematic analysis and descriptive statistics to analyze each data set. RESULTS: Nine students took part in three group discussions, and completed an online survey. While students reported positive learning experiences overall, those attending virtually struggled with the synchronous elements of the hybrid model. Virtual students reported developing research skills not offered through courses at their home institution, and students attending the course in person benefited from the diverse perspectives of distance learners. All stressed the need to foster a sense of community. CONCLUSIONS: Quality delivery of online graduate education in family medicine research requires optimizing social exchanges among virtual and in-person learners, ensuring equitable engagement among all students, and leveraging the unique tools afforded by online platforms to create a shared sense of a learning community.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".