Mobile Audience Response Systems at a Continuing Medical Education Conference
Bibliographic record
Abstract
BACKGROUND: Mobile audience response systems (mARS) are electronic systems allowing speakers to ask questions and audience members to respond anonymously and immediately on a screen which enables learners to view their peers' responses as well as their own. mARS encourages increased interaction and active learning. OBJECTIVES: This study aims to examine the perceptions of audience members and speakers towards the implementation of mARS at a national medical conference. METHODS: mARS was implemented at the CSO Annual Meeting in Winnipeg 2015. Eleven presenters agreed to participate in the mARS trial. Both audience and presenters received instructions. Five-point Likert questions and short answer questions were emailed to all conference attendees and the data was evaluated. RESULTS: Twenty-seven participants responded, 23 audience members and 4 instructors. Overall, responders indicated improved attention, involvement, engagement and recognition of audience's understanding of topics with the use of mARS. mARS was perceived as easy to use, with clear instructions, and the majority of respondents expressed an interest in using mARS in more presentations and in future national medical conferences. Most respondents preferred lectures with mARS over lectures without mARS. Some negative feedback on mARS involved dissatisfaction with how some presenters implemented mARS into the workshops. CONCLUSION: Overall mARS was perceived positively with the majority of respondents wanting mARS implemented in more national medical conferences. Future studies should look at how mARS can be used as an educational tool to help improve patient outcomes.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".