Measuring continuing medical education conference impact and attendee experience: a scoping review
Bibliographic record
Abstract
Objectives: The aim was to comprehensively identify published research evaluating continuing medical education conferences, to search for validated tools and perform a content analysis to identify the relevant domains for conference evaluation. Methods: We used scoping review methodology and searched MEDLINE® for relevant English or French literature published between 2008 and 2022 (last search June 3, 2022). Original research (including randomized controlled trials, non-randomized studies, cohort, mixed-methods, qualitative studies, and editorial pieces) where investigators described impact, experience, or motivations related to conference attendance were eligible. Citations were assessed in triplicate, and data extracted in duplicate. Results: Eighty-three studies were included, 69 (83%) of which were surveys or interview based, with the majority conducted at the end of or following conference conclusion. Of the 74 tools identified, only one was validated and was narrowly focused on a specific conference component. A total of 620 items were extracted and categorized into 4 a priori suggested domains (engagement-networking, education-learning, impact, scholarship), and an additional 4 identified through content analysis (value-satisfaction, logistics, equity-diversity-inclusivity, career influences). Time trends were evident, including the absence of items related to equity-diversity-inclusivity prior to 2019, and a focus on logistics, particularly technology and virtual conferences, since 2020. Conclusions: This study identified 8 major domains relevant for continuing medical education conference evaluation. This work is of immediate value to individuals and organizations seeking to either design or evaluate a conference and represents a critical step in the development of a standardized tool for conference evaluation.
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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 | 0.078 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.036 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".