A Mixed Learning Technology Approach for Continuing Medical Education
Bibliographic record
Abstract
INTRODUCTION: Distance learning technologies have been used for many years to provide CME to rural physicians. The purpose of this study was to evaluate the utility and acceptability of a mixed learning technology approach for providing distance CME. The approach combined audio teleconferencing instruction with a Web-based learning system enabling the live presentation and archiving of instructional material and media, asynchronous computer conferencing discussions, and access to supplemental online learning resources. METHODOLOGY: The study population was comprised of physicians and nurse practitioners who participated in audio teleconference sessions, but did not access the Web-based learning system (nonusers); learners who participated in audio teleconferences and accessed the Web-based system (online users); and faculty. The evaluation focused upon faculty and learners' experiences and perceptions of the mixed learning technology approach; the level of usage; and the effectiveness of the approach in fostering non-mandatory, computer-mediated discussions. RESULTS AND DISCUSSION: The users of the Web-based learning system were satisfied with its features, ease of use, and the ability to access online CME instructional material. Learners who accessed the system reported a higher level of computer skill and comfort than those who did not, and the majority of these users accessed the system at times other than the live audio teleconference sessions. The greatest use of the system appeared to be for self-directed learning. The success of a mixed learning technology approach is dependent on Internet connectivity and computer access; learners and faculty having time to access and use the Web; comfort with computers; and faculty development in the area of Web-based teaching.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".