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The Development and Evaluation of Online Neuroanatomy Resources for OT Students

2022· article· en· W4225377290 on OpenAlexaff
Michael Crisostimo, Michele Barbeau

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsCohortPerceptionNeuroanatomyOccupational therapyPsychologyMedical educationMedicineMathematics educationNeurosciencePathologyPsychiatry

Abstract

fetched live from OpenAlex

Of necessity for a practicing Occupational Therapist is the ability to demonstrate competent neuroanatomical knowledge through application into clinical practice. Yet, throughout the academic careers of Occupational Therapy (OT) students, neuroanatomy (NA) will consistently be perceived as one of the most difficult anatomical topics encountered. Rising in anatomical education is the use of computer assisted learning (CAL) due to its alignment with educational goals and student desires. This study aims to determine how neuroanatomy e‐modules (NEMs) can improve the perceptions and academic performance in NA of first year Master of Occupational Therapy (MScOT) students at Western University. In this descriptive study, 1st year MScOT students who studied NA without the use of NEMs (N = 61, nonNEM cohort) completed a Likert style survey examining perceptions of major NA topics. In addition, multiple choice exams (N = 109 questions) were analyzed to measure student performance on questions regarding NA topics, lower or higher order complexity, and clinical neurology. Four NEMs were then created and implemented into the next student cohort (N = 75, NEM cohort). Upon completion of the course, students were once again surveyed for perceived NA difficulty and final exam performance analyzed (N = 34). The overall self‐perceived NA knowledge of the students exposed NEMs were similar in comparison to non‐NEM cohort (p = .811), however, the cohort exposed to the NEMs demonstrated decreased perceived difficulty on all six NA topics surveyed, with a significant difference being found on cranial nerve difficulty (p = .007). Consistent with non‐NEM cohort, students in the NEM cohort still perceived the basal ganglia & cerebellum, and spinal cord as the most difficult NA topics. When measuring academic performance, students in the NEM cohort demonstrated significant improvements in overall NA (p = .001), higher‐order NA (p = .001), and clinical neurology knowledge (p = .009), however no improvement in lower‐order NA knowledge was found (p =.122) Further, of the NA topics examined, students in the NEM cohort demonstrated significant improvement on cranial nerve‐based questions (p = .01) in comparison to the non‐NEM cohort. With the implementation of NEMs, the data from the present study demonstrates the efficacy of CAL emodules in improving NA performance and perceptions within OT curriculums. Of most significance is the improvement on higher order NA performance with the use of NEMs, as questions of this nature require greater critical thinking and application of knowledge, closely resembling what students will encounter in their clinical practice. Although further research on the interaction between performance and perceptions is still required, this study shows initial evidence of a relationship as students significantly improved their perceptions and performance, especially when it came to cranial nerve‐based questions. With the shift and modernization of educational standards, OT instructors should consider the implementation of CAL emodules into their curriculums as it can have significant benefits in the outcomes of OT students learning NA and their role in the treatment and rehabilitation of those with neurological conditions.

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 score0.987
Threshold uncertainty score0.278

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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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Citations1
Published2022
Admission routes1
Has abstractyes

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