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Record W4392385452 · doi:10.1016/j.tria.2024.100292

The use of an online anatomy laboratory for allied health education

2024· article· en· W4392385452 on OpenAlexaff
Kapilan Panchendrabose, Micah Grubert Van Iderstine, Alexa Hryniuk

Bibliographic record

VenueTranslational Research in Anatomy · 2024
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSurface anatomyMedical educationMedicineHuman anatomyPharmacyPsychologyAnatomyNursing

Abstract

fetched live from OpenAlex

In-person cadaveric anatomy laboratories allow for students to learn the intricacies of the human body but also develop skills related to communication, clinical reasoning, and interprofessional collaboration. However, the COVID-19 pandemic caused a shift from in-person course delivery to an online medium. Therefore, the objective of this study was to develop and evaluate the implementation and use of an online anatomy laboratory as a replacement for an in-person laboratory component. An anatomy course for allied heath students (pharmacy and respiratory therapy) that included an in-person cadaveric laboratory was modified for online delivery. The laboratory component utilized cadaveric images presented by the instructor and breakout rooms for small group discussion to simulate in-person anatomy laboratory experiences. Online anatomical studies had no academic advantage or disadvantage compared to in-person instruction. Additionally, students indicated that the online laboratories were enjoyable and helpful for learning anatomy, rated the guided cadaveric image portion very highly and responded positively to the helpfulness of breakout room sessions in learning anatomy. Based on the results of this study, online delivery of an anatomy laboratory, that was developed to simulate important aspects of in-person learning, can act as a viable alternative-learning platform for anatomical laboratory 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.164
GPT teacher head0.459
Teacher spread0.295 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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