The Difficulty of Recruiting Speakers for Continuing Medical Education
Bibliographic record
Abstract
BACKGROUND: Obtaining speakers for various continuing medical education (CME) programs can be a challenging and sometimes frustrating process. PURPOSE: The goal of this study was to quantify the difficulty in recruiting speakers for CME. METHODS: A retrospective review of planning documents from three CME programs for family physicians was conducted. Descriptive analysis and analysis of variance testing was performed on the data collected. RESULTS: In all three programs, obtaining speakers has become more difficult over the past 3 years with 1.75 [standard deviation (SD)=1.46] to 2.32 (SD=1.85) mean requests. Finding speakers for rural programs is more challenging than local CME sessions with 2.11 (SD=1.78) compared with 1.68 (SD=1.12) mean requests, respectively. University faculty represent 45.7% of CME speakers. CONCLUSIONS: This is the first study to document the increasing difficulty of recruiting speakers for CME. This significant difficulty in speaker recruitment has several implications for the CME offices and physician learners.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".