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P32 Using simulation to prepare medical students to assess and manage an acutely unwell and suicidal patient

2019· article· en· W2987104779 on OpenAlexaboutno aff
Ella Mcgowan, Helen Leach

Bibliographic record

VenuePoster presentations · 2019
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMedical emergencyMedicine

Abstract

fetched live from OpenAlex

<h3>Background</h3> Simulation in undergraduate medical education often focuses on the assessment and management of an acutely unwell patient<sup>1</sup>, whilst communication skills needed to assess a mentally unwell patient tends to be taught through role player<sup>2</sup>. We wanted to combine the two presentations to reflect on real life practice and to help prepare the students for assessment of an acutely medically ill patient who also is also suicidal. <h3>Summary of work</h3> This simulation session was implemented for 67 final year medical students on the Acutely Ill Patient (AIP) module. The scenario involved a faculty member acting as a patient who had taken a large overdose of paracetamol and alcohol with the intention of ending their life, who was initially compliant with the clinical assessment but then decides they want to go home. The patient had pictures of cuts attached to her forearms and appeared disheveled. The scenario was developed for the students to practice assessing capacity and risk whilst continuing with medical management in a safe space where the situation would be debriefed with trained faculty and a toxicology consultant. <h3>Summary of results</h3> The students’ opinions were collected using SurveyMonkey via QR codes and we received a total of 48 responses. We found that 56% of the students strongly agreed with the statement ‘I feel more confident managing acutely ill patients with this presentation’ and 60% strongly agreed with the statement ‘I am more aware of the non-technical skills required in clinical practice’. One student felt ‘the simulation was very useful and a different style to most enabling communication to be explored more fully’. <h3>Discussion and conclusions</h3> Our students were able to practice assessing a patient who was refusing medical treatment whilst lacking mental capacity and they appreciated the need for sensitive communication and the importance of utilising the other members of the team including security services. Our faculty member, who was a Clinical Teaching Fellow, was able to act as a tearful then angry patient and we recognise that not all departments would have faculty with these skills. For this scenario to be successful the students should have prior experience in simulation. <h3>Recommendations</h3> Simulation can be used to aid in the teaching of risk assessment for a suicidal patient in addition to the medical management. When communication is a vital part of assessment a real person playing the patient can improve the fidelity. <h3>References</h3> Issenberg S.B., McGaghie W.C., Petrusa E.R., Gordon L. D., Scalese R.J. (2005) ‘Features and uses of high- fi delity medical simulations that lead to effective learning: a BEME systematic review’, <i>Medical Teacher</i>27 : 10 – 28 McNaughton, N., Ravitz, P., Wadell, A., Hodges, B. (2008). ‘Psychiatric Education and Simulation: A Review of the Literature.’ <i>The Canadian Journal of Psychiatry</i>, 53(2), pp.85–93.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.054
GPT teacher head0.417
Teacher spread0.363 · 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".

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Published2019
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