Current state of distance continuing medical education in North America
Bibliographic record
Abstract
BACKGROUND: Every continuing medical education (CME) provider is confronted one day or another with deciding whether to develop distance education programs that may enhance access to CME for health professionals. To make a judicious decision, one needs to understand the features of distance education and the experiences of other providers. METHODS: Since there was a lack of information in the literature regarding the actual state of distance CME in North America, a Web-based survey aimed at CME providers was conducted including a description of the providers, the users, the activities offered, the technologies employed, and the administration of the systems. RESULTS: The results from this study indicate that the majority (68%) of CME providers had not developed distance education programs at the time of the survey; 30% of the providers, mainly from private companies, were offering nondegree distance education programs, and 2% of the university providers were offering degree programs. The technologies mainly used to develop distance education programs were printed material (69%), videoconferencing (58%), and, to a lesser degree, videotape. The revenue sources to develop degree programs were government funding, tuition, and fees. Other sources such as commercial support and sales were used for nondegree programs. IMPLICATIONS: This study revealed that there was considerable interest in distance education, especially from the organizations not offering this type of program. Since distance CME features are now better known, this is a step toward the advancement and development of more and better distance education programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".