Content Reinforcement of the Cardiovascular Physiology Improves Knowledge Retention in Nursing Students
Bibliographic record
Abstract
There is growing concern over the loss of anatomical and physiological knowledge in medical, allied‐health & nursing students over time (Narnaware & Neumeier, 2020a, Narnaware, Y. 2021). Numerous studies have demonstrated the difficulty of the students in these disciplines to retain and apply anatomical knowledge as they progress through their programs of study (Narnaware and Neumeier, 2020a). However, physiological knowledge retention has not been studied as extensively as anatomical knowledge retention in health care disciplines, with very few studies focusing on nursing students (Aari et al., 2004). Of those studies, most are carried out after graduation (Aari et al., 2004) or are focused on a single or limited number of organ systems (Pourshanazari et al., 2013). We have previously shown that physiology students retained approximately 86.6% of their first‐year physiological knowledge over four months (Narnaware et al., 2020b). To improve the acquisition and retention of physiological knowledge, the present study aims to develop an interventional strategy that includes the repeated assessment of cardiovascular physiology and defenses knowledge over eight weeks. Nursing students were quizzed on two components of cardiovascular physiology (vascular system and blood) and defenses using the online quizzing system Kahoot. Each Kahoot quiz included 9‐11 knowledge and comprehension level multiple‐choice questions, and new sets of questions were used for each week’s Kahoot quiz. Data were statistically analyzed using SPSS II, and means were compared using 2‐sample t‐tests. The scores are described as the mean and standard deviation (SD) and are presented in figure 1 and table 1. Statistical significance was set at P < 0.05 for all tests. Compared to week 1, repeating knowledge of the vascular physiology and defenses yielded a significantly higher (P<0.05) knowledge retention at week 2 (8.4% & 11.7%). However, this retention was highest at weeks 3 (18.7% & 16.9%) and weeks 4 (21.6% & 14.3%), P<0.001) in both organ systems, with less significant improvement (P<0.05) at week 6 (13.3%) and no significant difference in defenses (4.6%). No significant differences in knowledge retention were found between vascular and defenses at week 8. However, compared to vascular physiology and defenses, content reinforcement of blood was highly significant at all weeks. Compared to week 1, knowledge retention of blood was highest at week 4 (69.5%), week 6 (55.2%), and week 8 (54.7%), P<0.0001), with less significant retention at week 2 (27.8%) and week 3 (31.2%), P<0.001). Although organ system‐specific improvements in knowledge retention were found, the study results show that repeated knowledge assessment can significantly improve knowledge retention of cardiovascular physiology and defenses in nursing students and agrees with previously reported studies in medical students (Pourshanazari et al., 2013). Therefore, content reinforcement should be used as one of the interventional strategies to improve knowledge retention in nursing students, and further research should be conducted to explore effective ways to maintain increased retention over more extended periods.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".