Family Medicine Journal Club: To Tweet or Not to Tweet?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Online journal clubs have recently become popular, but their effectiveness in promoting meaningful discussion of the evidence is unknown. We aimed to understand the learner experience of a hybrid online-traditional family medicine journal club. METHODS: We used a qualitative descriptive study to understand the experience of medical students and residents at the University of Toronto with the hybrid online-traditional family medicine journal club, including perceived useful and challenging aspects related to participant engagement and fostering discussion. The program, informed by the literature and needs assessment, comprised five sessions over a 6-month period. Learners led the discussion between the distributed sites via videoconferencing and Twitter. Six of 12 medical students and 33 of 57 residents participated in one of four focus groups. Thematic data analysis was performed using the constant comparison method. RESULTS: While participants could appreciate the potential of an online component to journal club to connect distributed learners, overall, they preferred the small group, face-to-face format that they felt produced richer and more meaningful discussion, higher levels of engagement, and a better learning opportunity. Videoconferencing and Twitter were seen as diminishing rather than enhancing their learning experience and they challenged the assumption that millennials would favor the use of social media for learning. CONCLUSIONS: Our study demonstrates that for discussion-based teaching activities such as journal club, learners prefer a small-group, face-to-face format. Our findings have implications for the design of curricular programs for distributed medical learners.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".