Anatomical Knowledge Retention in Second‐Year Bachelor of Science & Psychiatric Nursing Students
Notice bibliographique
Résumé
There is growing concern that nursing, medical and allied health students do not retain enough anatomical knowledge to confidently and successfully apply it in future classroom and clinical settings (Doomernik et al., 2017). Evidence now shows that knowledge retention is impacted by many factors including admission criteria, age, sex, ethnicity, prior knowledge of science/biology, a gap between high school and university, and health care discipline (McVicar et al., 2016; Vogl, 2017). In Canada, the discipline of nursing can be subdivided into three professional designations, each with different educational requirements; Registered Nurses, Licensed Practical Nurses, and Registered Psychiatric Nurses (Canadian Nurses Association, 2019). At MacEwan University students in the Psychiatric Nursing Diploma Program (PND) and the Bachelor of Science in Nursing Program (BScN) take the same first year anatomy course. With the understanding that discipline choice has a potential impact on knowledge retention, this study aimed to determine the overall difference in anatomical knowledge retention between second‐year PND students and second‐year BScN students, and if there is a difference based on organ system. To address these questions, second‐year PND and BScN students were quizzed on knowledge that was covered in the anatomy course. For each system, students were asked to answer nine to eleven knowledge and comprehension level multiple‐choice questions. The scores from these quizzes were compared to the first‐year examination scores on the same content to determine overall knowledge retention. Data were statistically analyzed using SPSS II, and means were compared using 2‐sample t‐tests and two‐way ANOVA. The scores are described for each organ system by reporting the mean and standard deviation (SD). The mean score of questions from all organ systems in year one was 81.16 ± 10.6 (SD). Comparing that score to matched test items in year two, there is a significant decrease in the overall mean score from 81.16 ± 10.6 (SD) to 57.86 ± 11.8 (SD) (P<0.01) in BScN students and 51.05 ± 6.06 (SD) (P<0.001) in PND students. This equates to a 76.7% retention rate in BScN students and 69.8% retention rate in PND students. Compared to year 1, organ‐specific knowledge retention levels varied between BScN students and PND students, however the highest retention and lowest retention systems were similar between both cohorts. The highest retention levels were seen in the gastrointestinal system (89.7% BScN; 80.5% PND), respiratory system (88.5% BScN; 86.3% PND), integumentary system (80.1% BScN; 72.7% PND) and special senses (78.7% BScN; 63.0% PND). Retention levels were lowest for the musculoskeletal system (69.3% BScN; 62.2% PND) and the vascular system (53.9% BScN; 62.8% PND). This demonstrates a significant decrease in knowledge retention in both PND and BScN students over the course of one year. Retention levels were organ system and cohort‐specific. PND students demonstrated a significantly lower overall retention rate, however, had a higher level retention in the low scoring vascular system, and less variance in retention levels between systems compared to BScN students.
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 | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».