Symposium 2: Blended Simulation Based Medical Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".