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Record W4384343231 · doi:10.7759/cureus.41869

Defining the Nature of Augmented Feedback for Learning Intraosseous Access Skills in Simulation-Based Health Professions Education

2023· article· en· W4384343231 on OpenAlexafffund
Julia Micallef, Dale Button, Alvaro Uribe Quevedo, Christopher McClatchey, Lindsey King, Adam Dubrowski

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsOntario Tech UniversityDurham College
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDelphi methodSession (web analytics)DelphiMedicineHealth careAugmented realityOutcome (game theory)Medical educationPerspective (graphical)Work (physics)Computer scienceKnowledge managementHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

In the field of health professions education, acquiring technical skills involves three stages: 1) receiving instructions, 2) engaging in practice, and 3) receiving feedback. Simulation serves as a valuable tool that encompasses all three stages, enhancing the effectiveness of health professions education. This work focuses on feedback, which can be categorized as intrinsic (perceived by the learner through their senses) or augmented (provided by an external perspective). Augmented feedback can take the form of knowledge of results (information regarding the outcome) or knowledge of performance (information about the actions leading to the outcome). The overall objective of this work was to evaluate the perceived efficacy of these types of feedback in learning technical skills using a simulation, specifically an intraosseous access simulator, among advanced care paramedics. The primary focus of this article and the initial step towards achieving the aforementioned objective of this work was to determine the possible knowledge of results and knowledge of performance that paramedic facilitators could offer to advanced care paramedics during the use of an existing intraosseous access simulator. This research was conducted following the design-based research framework, employing a combination of design thinking and Delphi methods to generate a comprehensive list of augmented feedback, in both the form of knowledge of results and knowledge of performance, that can be provided to advanced care paramedics while learning intraosseous access skills through a simulator. The design thinking session was carried out to generate an initial inventory of augmented feedback, which was then refined through two rounds of Delphi consensus-building with paramedic experts. This process resulted in an eight-step list of feedback for knowledge of results and knowledge of performance that can be delivered to advanced care paramedics by paramedic facilitators using an intraosseous access simulator.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.124
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.037
GPT teacher head0.450
Teacher spread0.413 · 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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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