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Record W4288066151 · doi:10.1097/sih.0000000000000543

Making Concepts Material

2021· article· en· W4288066151 on OpenAlexaff
Jeffrey J. H. Cheung, Kulamakan Kulasegaram, Nicole N. Woods, Ryan Brydges

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2021
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsThe Wilson CentreToronto Public Health
Fundersnot available
KeywordsCognitionKnowledge retentionComputer scienceCognitive loadKnowledge transferConceptual modelConceptual frameworkProcedural knowledgeKnowledge integrationMediationPsychologyKnowledge managementDomain knowledgeMedical educationMedicine

Abstract

fetched live from OpenAlex

Background Simulation affords opportunities to represent functional relationships between conceptual (eg, anatomy) and procedural knowledge (eg, needle insertion technique) in ways that make them accessible to our many senses. Despite deprioritizing realism, such simulations may encourage trainees to create cognitive connections between these knowledge (ie, cognitive integration), which may improve transfer of learning. However, the impact of such “integrated instruction” has not been examined in simulation-based training. We developed integrated video- and simulator-based instructional modules for lumbar puncture training and compared their impacts on participants' retention, transfer, and conceptual knowledge. Methods During 1 hour of simulation-based training, we randomized 66 medical students to receive either (a) video-based procedural-only instruction, (b) integrated video-based instruction, or (c) integrated simulator-based instruction. One week later, we tested participants' retention and transfer performances and their conceptual knowledge on a written test. Results Simple mediation analyses revealed that compared with participants receiving procedural-only instruction, participants receiving integrated instruction had superior retention and transfer outcomes, mediated by gains in conceptual knowledge (all P < 0.01). We found no significant differences between the integrated groups for retention, transfer, or conceptual knowledge (all P > 0.01). Conclusions We extended previous findings, showing integrated instruction (video- or simulator-based) improved trainees' conceptual knowledge, which mediated their improved retention and transfer. As an innovation, we demonstrated how simulators can facilitate cognitive integration by making abstract conceptual-procedural relationships material. In suggesting how researchers might capitalize further on simulator-based integration, we offer an alternative framework for designing simulations that emphasizes cognitive processes rather than simulator fidelity.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.191
Threshold uncertainty score0.640

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1910.046

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 head0.097
GPT teacher head0.463
Teacher spread0.366 · 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 designTheoretical or conceptual
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

Citations9
Published2021
Admission routes1
Has abstractyes

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