A comparative evaluation of the effect of internet-based CME delivery format on satisfaction, knowledge and confidence
Bibliographic record
Abstract
BACKGROUND: Internet-based instruction in continuing medical education (CME) has been associated with favorable outcomes. However, more direct comparative studies of different Internet-based interventions, instructional methods, presentation formats, and approaches to implementation are needed. The purpose of this study was to conduct a comparative evaluation of two Internet-based CME delivery formats and the effect on satisfaction, knowledge and confidence outcomes. METHODS: Evaluative outcomes of two differing formats of an Internet-based CME course with identical subject matter were compared. A Scheduled Group Learning format involved case-based asynchronous discussions with peers and a facilitator over a scheduled 3-week delivery period. An eCME On Demand format did not include facilitated discussion and was not based on a schedule; participants could start and finish at any time. A retrospective, pre-post evaluation study design comparing identical satisfaction, knowledge and confidence outcome measures was conducted. RESULTS: Participants in the Scheduled Group Learning format reported significantly higher mean satisfaction ratings in some areas, performed significantly higher on a post-knowledge assessment and reported significantly higher post-confidence scores than participants in the eCME On Demand format that was not scheduled and did not include facilitated discussion activity. CONCLUSIONS: The findings support the instructional benefits of a scheduled delivery format and facilitated asynchronous discussion in Internet-based CME.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".