Internet-Based Continuing Medical Education in Otolaryngology: A Survey of Canadian Otolaryngologists
Bibliographic record
Abstract
OBJECTIVE: To assess the readiness of the Canadian otolaryngology community for an on-line continuing medical education (CME) program. METHODS: Data were obtained through surveys mailed to members of the Canadian Society of Otolaryngology-Head and Neck Surgery. RESULTS: Two hundred and eight of 321 (65%) surveys were returned. Seventy-six percent of the respondents indicated that they would be interested in participating in an on-line CME course. A greater proportion of younger otolaryngologists and those with community-based practices were interested in participating. A greater number of those with an academic practice were found to have convenient access to the Internet, the ability to post images on-line, and encountered cases they felt worthy of discussion and were willing to instruct/facilitate future on-line CME. Ninety-three percent of respondents described having easy access to the Internet, but only 32% said that they have the equipment necessary to post computed tomographic scans/audiograms on-line. Twenty-three percent had previous computer-based CME experience. Sinusitis and related topics were of greatest interest, with hearing loss/ear surgery and oncology following respectively. CONCLUSIONS: The data suggest that on-line CME would be welcomed and are feasible in the Canadian otolaryngology community.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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 | 0.000 |
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".