Evaluating knowledge loss over multiple retention intervals can identify deficiencies and inform curricular development
Bibliographic record
Abstract
Nursing students struggle to retain enough anatomical knowledge to meet their entry to practice competencies, but what knowledge is missing and when this occurs has been previously unexplored. A cohort of 80 nursing students were given multiple choice quizzes to assess their anatomical knowledge on 11 different organ systems during their second, third, and fourth year. Results were analyzed in comparison to their first-year examination scores to determine knowledge loss. Results showed an overall knowledge loss of 33.5% in the second year, 31.8% in the third year, and 29.6% in the fourth year. There were significant differences in system specific results. Special senses (i.e., audition) had a 20.6% loss in the second year, increased in retention to a 17.3% loss in third year, and then decreased to a 37% loss in fourth year. The vascular system had a 46.1% knowledge loss at the second-year assessment, declined to 49% knowledge loss in the third year, but improved to 27.6% knowledge loss by the fourth year. A similar change was observed for the musculoskeletal system with second-year loss at 30.7%, third-year loss at 40.3%, and fourth-year loss at 26.6%. These data suggest there are significant differences in the amount of knowledge retained by nursing students depending on the system being tested and the year the test is taken. Identifying the areas and times where anatomical knowledge is lost and gained is valuable for instructors in any program so that specific topics can be targeted at different times with more effective educational strategies.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".