Social interaction and participation: Formative evaluation of online CME modules
Bibliographic record
Abstract
INTRODUCTION: This exploratory study examines Canadian physicians' participation in online social activities and learning discussions, perceptions of online social closeness, barriers and motivators to participation, and perceptions of the impact of course duration and face-to-face meetings on learning. METHODS: Formative evaluations were administrated to physicians participating in two online continuing medical education (CME) courses. Responses were recorded and calculated by the Blackboard Learning System. Content analysis was used to categorize comments and identify influencing factors. Online postings were counted to measure participation in the learning activities. RESULTS: The participation rate of 158 physicians and 10 facilitators in online social activities was very low. Forty-five percent of responding participants reported that more time for discussion would help them learn more; 62% stated that the initial face-to-face meeting helped improve online social relations and increase peer interactions online. Thirty-five percent of respondents reported participating in online social activities, while 29% had no time to do so, and 18% were not interested in doing so. Thirty-five percent felt closer or more connected to their peers after two discussion sessions; 11% did not feel closer because of their low participation; and 16% did not feel closer because of inadequate peer interaction. On the two evaluations, 49% and 22% of respondents, respectively, perceived lack of time and social bonding as major barriers to participating in learning discussion. DISCUSSION: Lack of time and peer response were given as the main reasons for low participation in social activity and learning discussions. Time and social bonding were major barriers to learning discussion. Course usefulness and participants' desire, commitment, and time management skills helped overcome barriers. Facilitators needed training in online systems and facilitation skills. Longer course duration and realistic pacing would probably foster more social interaction and greater course participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".