Web-based continuing medical education (II): Evaluation study of computer-mediated continuing medical education
Bibliographic record
Abstract
BACKGROUND: Over the years, various distance learning technologies and methods have been applied to the continuing medical education needs of rural and remote physicians. They have included audio teleconferencing, slow scan imaging, correspondence study, and compressed videoconferencing. The recent emergence and growth of Internet, World Wide Web (Web), and compact disk read-only-memory (CD-ROM) technologies have introduced new opportunities for providing continuing education to the rural medical practitioner. This evaluation study assessed the instructional effectiveness of a hybrid computer-mediated courseware delivery system on dermatologic office procedures. METHODS: A hybrid delivery system merges Web documents, multimedia, computer-mediated communications, and CD-ROMs to enable self-paced instruction and collaborative learning. Using a modified pretest to post-test control group study design, several evaluative criteria (participant reaction, learning achievement, self-reported performance change, and instructional transactions) were assessed by various qualitative and quantitative data collection methods. RESULTS: This evaluation revealed that a hybrid computer-mediated courseware system was an effective means for increasing knowledge (p < .05) and improving self-reported competency (p < .05) in dermatologic office procedures, and that participants were very satisfied with the self-paced instruction and use of asynchronous computer conferencing for collaborative information sharing among colleagues.
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 | 0.029 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".