The Nature of the Interaction Between Participants and Facilitators in Online Asynchronous Continuing Medical Education Learning Environments
Bibliographic record
Abstract
BACKGROUND: MDcme.ca offers an online asynchronous continuing medical education (CME) environment for family physicians. The nature of participation in online CME using computer-mediated conferencing (CMC) discussion systems and the characteristics of interaction between participants and facilitators is not well understood. PURPOSE: To examine the association between participant and facilitator participation in online asynchronous CME learning environments. METHODS: We analyzed registration and participation data including participant and facilitator postings to an asynchronous CMC discussion system for CME programs offered through MDcme.ca using frequency counts. Pearson r correlation was used to assess the association between numbers of participants and participant postings, facilitator postings and participant postings, and the number of discussion items accessed and participant postings. RESULTS: MDcme.ca offered 25 programs to 327 registrants, of whom 180 actually participated by accessing course materials and online discussion postings. Pearson r correlations showed a significant association between the number of participants and the number of postings, the number of facilitator postings and the number of participant postings, and the mean number of discussion items accessed by participants and the overall number of participant postings. DISCUSSION: Both the number of facilitator postings and the number of discussion items accessed by participants appeared to be important determinants of the amount of interaction that will occur in asynchronous online CME. Curriculum planners and facilitators of asynchronous CME need to be aware of the importance of fostering interactive, stimulating discussions if the potential of asynchronous learning for physicians is to reach its potential in supporting higher levels of critical, reflective, practice-based learning.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".