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Record W4403070582 · doi:10.54337/nlc.v9.9038

Symposium 2: Blended Simulation Based Medical Education

2014· article· en· W4403070582 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Conference on Networked Learning · 2014
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedical educationComputer scienceMathematics educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Simulation based medical education (SBME) is gradually becoming an inseparable part of medical and Professionals Allied to Medicine (PAM) education. The demand to use this training approach in healthcare is increasing every year to meet the Department of Health’s Standards for Better Health (NESC, 2008). As an alternative training approach SBME provides medical students and practitioners with near real-life opportunities to practice and improve clinical and non-clinical skills and improve health care services as a result. Although SBME is already a very popular training approach, Kneebone (2005) argues it is “often accepted uncritically, with undue emphasis being placed on technological sophistication at the expense of theory-based design” (p.549). SBME is “a complex service intervention” (McGaghie, 2009, p.50), which includes much more than a series of advanced technologies utilised for simulating an event. SBME is actualised by a network of closely knit human, non-human, and “conceptual and symbolic” (Bleakley, 2012, p.464) actors that work in an interrelated manner “as a basis to promoting learning and innovation” (Bleakley, p.464). It is not just the sophistication of the technology that supports learning but the dialogic relation of all the actors involved in creating the opportunities for learning. What is required to develop a ‘healthy’ and ‘growing’ network that promotes learning and innovation (Bleakley, 2012) or hinder effective learning hasn’t widely been investigated. Bleakley argues that actor network theory (ANT) “serves to repair the historical separation of theory and practice” (p. 465). To understand SBME as a complex process involving technology, people, objects, artefacts, actions, and places, ANT may introduce new insight, “an interruption or intervention, a way to sense and draw nearer” (Fenwick & Edwards 2010: ix) to the phenomenon of SBME. This paper expands the understanding of how actors interact with each other within a network and the practices that support/hinder blended learning in the Lancashire Teaching Hospitals NHS Trust (LTHTR) Simulation Centre (SC). Outcomes provide insight into the design of a simulation session, describe the assemblage of a blended learning in SBME (B-SBME) actor network, and illustrate an example of the network effects of mediators’ and intermediaries’ capacities to form alliances between a B-SBME networked assemblage and broader Trust networks.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.343
Teacher spread0.312 · 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