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Anatomical Knowledge Retention in Second‐Year Bachelor of Science & Psychiatric Nursing Students

2020· article· en· W3016428639 on OpenAlexaffabout
Yuwaraj Narnaware, Melanie Neumeier

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMacEwan University
Fundersnot available
KeywordsBachelorKnowledge retentionComprehensionPsychologyNursingMedicineMedical education

Abstract

fetched live from OpenAlex

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.

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)0.007
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.377
Teacher spread0.339 · 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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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