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Changes in Anatomy Lecture and Laboratory Instruction During Covid‐19

2021· article· en· W3169816885 on OpenAlexaff
Derek Harmon, Stefanie M. Attardi, Malli Barremkala, Danielle C. Bentley, Kirsten Brown, Jennifer F. Dennis, Haviva M. Goldman, Kelly M. Harrell, Barbie A. Klein, Christopher J. Ramnanan, Joan T. Richtsmeier, Gary J. Farkas

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Gross anatomyMedical educationBonferroni correctionPublic institutionMedicineWilcoxon signed-rank testHigher education2019-20 coronavirus outbreakPsychologyAnatomyMann–Whitney U testInternal medicineMathematicsPathologyStatisticsPolitical science

Abstract

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Introduction/Objective Covid‐19 created challenges to anatomy education, particularly gross anatomy given the traditional in‐person format of lectures and lab. The objective of this study was to assess the changes in lecture methods and lab materials used in anatomy courses that ran between May‐August (T1) and August‐December (T2) 2020 responding to Covid‐19 restrictions. Materials/Methods A survey was distributed to anatomy educators through professional associations from June‐November 2020. Respondents indicated (1) their institution; (2) programs taught (professional health (PH), medicine (MED), or undergraduate (UG)); (3) course type (integrated or stand‐alone); (4) percentage of lab time before and during Covid‐19 that utilized cadaveric, plastic, and/or other teaching materials; and (5) lecture format. Institutions were classified as public or private via institution websites. Mann‐Whitney U and Wilcoxon signed‐rank tests with Bonferroni correction compared responses before and during Covid‐19 across programs, course type, and institution. Data are presented as percent increase (+value) or decrease (‐value). Alpha<5%. Results T1 and T2 received 67 and 191 responses, respectively. During T1 and T2, cadaver use decreased in PH (‐58% & ‐28%), MED (‐55% & ‐34%), and UG (‐57% & ‐55%) programs (P≤0.045); stand‐alone (‐58% & ‐33%,P<0.001) and integrated (‐48% & ‐28%, P≤0.004) courses; and private (‐49% & ‐25%, P<0.001) and public (‐65% & ‐34%, P<0.001) institutions. During T1 and T2, plastic use did not change for programs, institutions, or courses (P>0.05), except UG decreased plastic usage during T2 (‐20%; P=0.033). During T1 and T2, use of other teaching materials increased in PH (+1180% & +278%), MED (+385% & +1000%), and UG (+285% & +246%) (P≤0.015); stand‐alone (+920% & +540%, P<0.001) and integrated (+330% & +500%, P≤0.002) courses; and private (+1233% & +667%, P<0.001) and public (+415% & +400%, P<0.001) institutions. For T1 and T2, in‐person lecture decreased (‐89% & ‐72%, P≤0.001), while remote lecture increased (+509% & +533%, P≤0.001) during Covid‐19. Conclusion Reduction in cadaver use and in‐person lecture were most pronounced in T1, but remained diminished through both time points, suggesting a shift from the initial pandemic response to teaching to more complex hybrid programs as regulations permitted. Significance/Implication This study provides evidence to better understand how anatomy educators adapted their gross anatomy teaching due to Covid‐19 across programs. In addition, this study provides first of its kind insight into how anatomy was taught across programs prior to Covid‐19. Future studies need to determine whether the findings characterized here were pandemic‐based or if they represent long‐term changes for anatomy education.

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)0.005
metaresearch head score (Gemma)0.019
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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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Citations0
Published2021
Admission routes1
Has abstractyes

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