Making it stick: use of active learning strategies in continuing medical education
Bibliographic record
Abstract
BACKGROUND: Despite the known benefits of active learning (AL), the predominate educational format in higher education is the lecture. The reasons for slow adaptation of AL in medical education are not well understood. The purpose of this survey was to determine knowledge, usage, attitudes, and barriers to AL use in academic Continuing Medical Education (CME). METHOD: A 20-item questionnaire was developed and sent with a link to an online questionnaire to the Society of Academic Continuing Medical Education (SACME) listserv of ~ 350 professionals representing academic medical centers, teaching hospitals, and medical specialty societies in the United States (U.S.) and Canada. Responses were collected with SurveyMonkey® from October-November, 2019. Data were analyzed using SPSS®. RESULTS: Responses from 146 SACME members in 91 CME units yielded a ~ 42% survey response rate. Many respondents reported their self-perceived knowledge of AL as high. Advanced training (e.g., certificate, Master of Education degree) was positively correlated with AL knowledge. AL methods were reportedly used in half of the CME activities in the majority (80%) of institutions. Higher levels of self-perceived knowledge were correlated with an increased percentage of AL-related CME activities. Commonly perceived barriers to use of AL were presenters' lack of familiarity and a need for more time-consuming preparation. CONCLUSIONS: More efforts are needed to increase innovation and incorporate evidence-based AL strategies in medical education, especially to foster learner engagement, critical thinking, and problem-solving ability.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".