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Gross Anatomy Dissection Improves Exam Scores Amongst Medical and Allied Health Students

2016· article· en· W2601496777 on OpenAlexaff
Rebekah J. Anders, Kelli Wheeler

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsGross anatomyDissection (medical)MedicineMedical educationAnatomyMedical physicsPsychology

Abstract

fetched live from OpenAlex

Introduction Student dissections are fundamental to gross anatomy. However, as medical curricula are revised and basic science disciplines integrated within the preclinical years, time allocated to teaching gross anatomy has been reduced. Having students alternate dissections is one approach to address these limitations. Purpose & Hypotheses The purpose of this study was to evaluate the effect that alternating dissections has on the academic performance of medical and allied health students taking gross anatomy. We hypothesized that students who participate in a particular dissection will perform better on questions that correlate to their dissection on both written and laboratory practical examinations compared to students who did not take part the dissection. We also hypothesized that lower performing students will benefit more from actively dissecting than higher performing students. Methods At the Medical College of Georgia, medical students take gross anatomy as part of an integrated systems‐based curriculum that spans the first year. Allied health students (physical therapy, occupational therapy, and physician assistant programs) take gross anatomy as a 9‐week course in the summer. In this study we compared written and laboratory exam performance of medical students (n=384 from 2013–2014 & 2014–2015) and allied health students (n=253 from 2014 & 2015) who dissected specific labs with those who did not, on the material related to the specific dissection. A repeated measures ANOVA was used to determine if dissection roles affected exam scores. To determine if low performing students would benefit more from dissecting than higher performing students, the class was divided into high and low performers based on median overall practical and written grades. The results were assessed using an ANCOVA. Results Our results for allied health showed that overall dissectors performed better than nondissectors on the lab exam. (80.6% vs. 79.0%; p<0.05). However, dissecting did not affect their performance on written exam questions (84.05% vs. 83.5%). Low performing students tested significantly better on both written and lab exams when they dissected compared to when they did not (lab: 74.4% vs. 72.5%; written: 78.5% vs. 77.2%; p<0.05); high performing students performed similarly, regardless of whether they dissected (89.3% vs 89.4%). Furthermore, medical students performed better on both lab and written exams related to content that they dissected (lab: 84.25% vs. 81.8%; written: 81.15% vs. 79.8%; p<0.001). Both high and low performing medical students benefitted from dissection, scoring significantly better on content they dissected on the lab exam (high: 88.8% vs. 86.7%; low: 79.45% vs. 76.7%; p<0.0001). In addition, low performing students also did better on written exam questions covering the content that they dissected (75.3% vs. 72.9%; p<0.0001). Conclusions Participation in dissection may give low performing students the opportunity to learn from their peers and professors during dissection time resulting in an increased lab and written performance on questions related to the dissection material. These students may also benefit from structured time in the lab as opposed to reviewing the lecture on their own. The academic improvement due to dissection validates the importance of gross anatomy dissection in the health care professional curriculum.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.267
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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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Citations0
Published2016
Admission routes1
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