206Clinical Epidemiology Knowledge Retention in Accelerated Teaching and Learning
Bibliographic record
Abstract
Abstract Background Accelerated courses continue to be part of the changing landscape in medical education despite limited evidence to support their efficacy in relation to knowledge retention. The purpose of this study was to determine if a difference in knowledge retention exists over time between students enrolled in a traditional versus an accelerated clinical epidemiology course. Methods The current study incorporated an epidemiologic prospective cohort design. The course in clinical epidemiology focused on evidence-based decision making for diagnostic and therapeutic research methods and problem-based learning. Knowledge retention was assessed at four-times points (baseline, three, six and 12 months) for students enrolled in either traditional (13 weeks) or accelerated (1 week) courses. Linear mixed-effect regression modeling was incorporated to examine the change in trajectory of knowledge retention over four points in time between students enrolled in traditional and accelerated teaching formats. Results A significant main effect of traditional versus accelerated course format on retention of knowledge over time was not found (β=-0.341, p = 0.410), suggesting that knowledge retention is not compromised regardless of teaching format. Furthermore, the greatest diminished knowledge retention was observed between baseline and 12 months (β = 10.595, p < 0.0001), followed by three months (β = 3.864, p < 0.0001) and six months (β = 1.180, p < 0.0001). Conclusion This study determined that accelerated course format does not compromise short- and long-term clinical epidemiology knowledge retention in students. Key message University administrators and faculty should not be suspicious of knowledge retention issues in accelerated courses and should endorse accelerated learning opportunities.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".