Physiological Knowledge Retention in Second‐Year Nursing Students
Notice bibliographique
Résumé
Despite anatomy and physiology being foundational courses in medical, nursing, and allied ‐health care programs, there is growing concern that the knowledge in these courses is not being retained by students over time. Numerous studies have demonstrated the difficulty of medical, allied health, and nursing students to retain and apply anatomical knowledge in their future years of study (Doomernik et al., 2017). 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 nursing students retained 92.0% of their first‐year physiological knowledge, losing 8.% within 4‐months (Narnaware and Neumeier, 2020a). The present study aims to determine the level of physiological knowledge retained by nursing students in the second year. To answer this question, nursing students were quizzed on ten organ systems using the on‐line quizzing system Kahoot. Each Kahoot quiz included nine to eleven knowledge and comprehension level multiple‐choice questions. These scores were compared to first‐year quiz scores on the same content to determine overall knowledge retention over a year. Data were statistically analyzed and means were compared using 2‐sample t‐tests. The scores are described for each organ system by reporting the mean and standard deviation (±SD). Statistical significance was set at P < 0.05 for all tests. The mean score of questions from all organ systems in year one was 62.9 ± 10.5 (±SD). Comparing that score to matched test items evaluated in the pathophysiology course, there is a decrease in the overall mean score from 62.9 ± 10.5 (±SD) to 47.9 ± 9.2 (±SD). This equates to an 85.0% retention rate, or 15.0% knowledge loss within a year. Organ‐specific knowledge retention was highest for digestive physiology (97.37%), respiratory physiology (92.32%), fluid and electrolyte physiology (90.41%), inflammation (85.99%), reproductive physiology (83.55%), and vascular physiology (85.22%). This was followed by renal physiology (83.37%) and blood (82.49%). Retention was comparatively lower for endocrine physiology (79.47%) and defenses (70.42%). These results demonstrate a high level of knowledge retention overall, with variations in retention being system‐specific. The level of knowledge retention in this study was significantly higher than previous rates reported in medical and allied‐health students (Pourshanazari et al., 2013) and is significantly higher than anatomical knowledge retention levels in the same population (Narnaware and Neumeier, 2020b). This study identifies where nursing students' knowledge retention gaps exist which will help to develop an interventional strategy for nursing students in the future.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».