Toward Practice-Based Continuing Education Protocols
Bibliographic record
Abstract
INTRODUCTION: Using assessment to facilitate learning is a well-established priority in education but has been associated with variable effectiveness for continuing professional development. What factors modulate the impact of testing in practitioners are unclear. We aimed to improve capacity to support maintenance of competence by exploring variables that influence the value of web-based pretesting. METHODS: Family physicians belonging to a practice-based learning program studied two educational modules independently or in small groups. Before learning sessions they completed a needs assessment and were assigned to either sit a pretest intervention or read a relevant review article. After the learning session, they completed an outcome test, indicated plans to change practice, and subsequently documented changes made. RESULTS: One hundred twelve physicians completed the study, 92 in small groups. The average lag between tests was 6.3 weeks. Relative to those given a review article, physicians given a pretest intervention: (1) reported spending less time completing the assigned task (16.7 versus 25.7 minutes); (2) performed better on outcome test questions that were repeated from the pretest (65.9% versus 58.7%); and (3) when the learning module was completed independently, reported making a greater proportion of practice changes to which they committed (80.0% versus 45.0%). Knowledge gain was unrelated to physicians' stated needs. DISCUSSION: Low-stakes formative quizzes, delivered with feedback, can influence the amount of material practicing physicians remember from an educational intervention independent of perceptions regarding the need to engage in continuing professional development on the particular topic.
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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 | 0.151 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.034 |
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".