Third-Year Nursing Student's Physiological Knowledge Retention
Notice bibliographique
Résumé
Anatomy and physiology are considered foundational courses in medical, nursing and allied-health care programs. However, there is a growing concern that students struggle to retain this essential knowledge over time. Numerous studies have demonstrated the difficulty of medical, nursing and allied healthcare students to retain and apply anatomical knowledge as they progress through their programs 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 conducted after graduation (Aari et al., 2004) or are focused on a single or a limited number of organ systems (Pourshanazari et al., 2013). The present study aims to determine the level of physiological knowledge retained by nursing students in the third year between completing their physiology course in first-year nursing and third-year Nursing Care of Families with Young Children course. To answer this question, nursing students were quizzed on ten organ systems using the online 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 two years. Data were statistically analyzed using SPSS II, 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 62.89 ± 10.49 (±SD). Comparing that score to matched test items evaluated in the Nursing Care of Families with Young Children course, there is a decrease in the overall mean score from 62.89 ± 10.49 (±SD) to 50.95 ± 13.02 (±SD). This equates to an 88.06% retention rate, or 11.94% knowledge loss within two years. Organ-specific knowledge retention was highest for inflammation (100%), respiratory physiology (99.10%), and vascular physiology (95.01%), followed by blood (89.16%), digestive physiology (86.28%), endocrinology (83.76%), defences (82.50%) and renal physiology (82.19%). Retention was comparatively lower for fluid and electrolyte balance (79.36%) and reproductive physiology (77.54%). 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 higher than anatomical knowledge retention levels in the same population (Narnaware and Neumeier, 2021). However, knowledge retention in the third year is not significantly different from the second year (Narnaware et al., 2021). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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 | 0,000 | 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 ».