Long-Term Knowledge Retention of Biochemistry Among Medical Students in Riyadh, Saudi Arabia: Cross-Sectional Survey
Bibliographic record
Abstract
Background: Biochemistry is a cornerstone of medical education. Its knowledge is integral to the understanding of complex biological processes and how they are applied in several areas in health care. Also, its significance is reflected in the way it informs the practice of medicine, which can guide and help in both diagnosis and treatment. However, the retention of biochemistry knowledge over time remains a dilemma. Long-term retention of such crucial information is extremely important, as it forms the foundation upon which clinical skills are developed and refined. The effectiveness of biochemistry education, and consequently its long-term retention, is influenced by several factors. Educational methods play a critical role; interactional and integrative teaching approaches have been suggested to enhance retention compared with traditional didactic methods. The frequency and context in which biochemistry knowledge is applied in clinical settings can significantly impact its retention. Practical application reinforces theoretical understanding, making the knowledge more accessible in the long term. Prior knowledge (familiarity) of information suggests that it is stored in long-term memory, which makes its retention in the long term easier to recall. objectives: This investigation was conducted at King Saud bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. The aim of the study is to understand the dynamics of long-term retention of biochemistry among medical students. Specifically, it looks for the association between students' familiarity with biochemistry content and actual knowledge retention levels. Methods: A cross-sectional correlational survey involving 240 students from King Saud bin Abdulaziz University for Health Sciences was conducted. Participants were recruited via nonprobability convenience sampling. A validated biochemistry assessment tool with 20 questions was used to gauge students' retention in biomolecules, catalysis, bioenergetics, and metabolism. To assess students' familiarity with the knowledge content of test questions, each question is accompanied by options that indicate students' prior knowledge of the content of the question. Statistical analyses tests such as Mann-Whitney U test, Kruskal-Wallis test, and chi-square tests were used. Results: Our findings revealed a significant correlation between students' familiarity of the content with their knowledge retention in the biomolecules (r=0.491; P<.001), catalysis (r=0.500; P<.001), bioenergetics (r=0.528; P<.001), and metabolism (r=0.564; P<.001) biochemistry knowledge domains. Conclusions: This study highlights the significance of familiarity (prior knowledge) in evaluating the retention of biochemistry knowledge. Although limited in terms of generalizability and inherent biases, the research highlights the crucial significance of student's familiarity in actual knowledge retention of several biochemistry domains. These results might be used by educators to customize instructional methods in order to improve students' long-term retention of biochemistry information and boost their clinical performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".