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Do Dissection‐ and Prosection‐based Laboratories Offer Comparable Learning Experiences? An Exploration of Student Learning in Two Laboratory Cohorts at the University of Guelph

2018· article· en· W2958964904 on OpenAlexaffabout
Sean McWatt, Genevieve Newton, Lorraine Jadeski

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedical educationPsychologyDissection (medical)MedicineMathematics educationSurgery

Abstract

fetched live from OpenAlex

Historically, human anatomy has been taught using lectures and dissection‐based (DI) cadaver laboratories. However, many institutions face challenges such as limited financial resources, inadequate cadaver availability, and curricular time constraints that limit the use of DI laboratories. Consequently, prosection‐based (PRO) environments have been favoured as less resource‐intensive alternatives to DI. The benefits and drawbacks of using either DI or PRO have been debated for decades; however, since most institutions employ only one of these instructional environments, direct quantitative comparisons are rarely available. The University of Guelph offers a comprehensive human anatomy course in the third‐year of the Human Kinetics and Biomedical Science undergraduate degree programs that includes both DI and PRO cohorts. All students attend the same lectures and complete the same examinations, but are enrolled in either a DI or PRO laboratory. PRO students learn from the donors dissected by their DI peers, and therefore witness a ‘slow reveal’ of structures throughout the course. In the present study, course experience (CE), student approach to learning (SAL), and course performance, were compared between students enrolled in DI (n = 147) and PRO (n = 44). CE was measured using the course experience questionnaire (CEQ), preferred (p‐) and contextual (c‐) deep (DA) and surface (SA) learning approaches were measured using the revised two‐factor study process questionnaire (R‐SPQ‐2F), and grades on laboratory tests (LT), written tests (WT), and in‐laboratory oral assessments (LOA) were analyzed alongside final course grades as performance outcomes. No significant differences in scores on any CEQ subscale were found between students in DI or PRO ( p > 0.05). There was no significant main effect of laboratory type on SAL [ F (1,189) = 0.119, p = 0.731, partial η 2 = 0.001], but PRO students reported significantly lower cSA scores than pSA scores [ F (1,43) = 5.89, p = 0.020, partial η 2 = 0.120] and DI students had significantly higher cDA scores than pDA scores [ F (1,146) = 21.55, p < 0.0005, partial η 2 = 0.129]. Furthermore, the main effect of SAL type indicated that both groups adopted significantly higher DA scores than SA scores [DA = 34.95 ± 0.494, SA = 23.06 ± 0.480, F (1,189) = 181.54, p < 0.0005, partial η 2 = 0.490]. Multiple linear regression (MLR) analyses with demographic data, preferred SAL scores, and CEQ subscale scores as covariates revealed that although laboratory type did not significantly influence the prediction of cSA scores ( p = 0.413), participation in DI was positively associated with cDA score (β = 1.474 ± 0.724, p = 0.043). There were no significant differences in grades on LTs or WTs between DI and PRO students at p ≤ 0.05; however, DI students performed significantly better on LOAs than PRO students (DI = 91.96% ± 0.357, PRO = 87.09% ± 0.829, p < 0.0005) and had higher resulting final grades (β = 1.842 ± 0.885, p = 0.039). These findings suggest that DI and PRO laboratories both foster stronger DA than SA to learning, thus PRO may serve as an acceptable method of human anatomy instruction at institutions with limited resources. However, DI may better promote skills used in oral assessment such as communication, teamwork, and problem‐solving. This abstract is from the Experimental Biology 2018 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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.264

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 head0.018
GPT teacher head0.269
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 teacher head, 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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Citations0
Published2018
Admission routes2
Has abstractyes

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