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Anatomical Knowledge Retention in Second‐Year Nursing Students

2019· article· en· W3172521807 on OpenAlexaff
Yuwaraj Narnaware, Melanie Neumeier

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCurriculumTest (biology)Statistical significanceMedicineMultiple choiceMedical educationSet (abstract data type)Health careHealth professionalsPsychologyNursingSignificant differenceInternal medicinePedagogyBiologyComputer science

Abstract

fetched live from OpenAlex

Human anatomy and physiology are foundational courses in undergraduate medical, allied health, and nursing curricula, and the significance of these courses in preparing health care professionals cannot be underestimated. However, despite the essential nature of these courses, there is growing concern that students do not retain enough anatomical knowledge to successfully apply it in future classroom and clinical settings. While there are many studies examining anatomical knowledge retention in medical and allied health students, no studies were found that address this concern in nursing students. The aim of this study was to determine how much anatomical knowledge second‐year nursing students retain from their first‐year anatomy class, and to determine if there is a difference in level of retention based on organ system. To address these questions, nurses in their second‐year health assessment course were quizzed on anatomical knowledge that was covered in the first year of their program. For each system students were asked to answer nine to eleven multiple‐choice questions (MCQs). The scores from these quizzes were compared to their 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. 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 the organ systems in year one was 83.05 ± 8.34 (SD). Comparing that score to matched test items from this cohort in year two, there is a significant decrease in the overall mean score from 83.05 ± 8.34 (SD) to 54.36 ± 12.9 (SD), t=6.14, P=0.0001). This equates to a 71.3% retention rate and a 28.7% knowledge loss after one year. System specific knowledge retention was highest for the gastrointestinal system (89.7%), respiratory system (88.5%), and genitourinary system (83.6%). This was followed by the integumentary system (80.1%), special senses (79.4%), nervous system (74.9%) and musculoskeletal system (69.3%). Retention was lowest for the lymphatic system (64.3%), cranial nerves (58.8%), vascular system (53.9%) and head and neck lymphatic (42.6%). The present study shows that nursing students' anatomical knowledge retention was comparatively higher than rates reported by others in medical students and allied‐health students. Retention levels were system specific. This study is now investigating knowledge retention in 3 rd and 4 th year nursing students. Further investigation into why retention is higher for specific systems and interventional strategies to improve knowledge acquisition and retention in nursing students is recommended. Support or Funding Information None This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.009
GPT teacher head0.261
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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