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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

Background Simulation in undergraduate medical education often focuses on the assessment and management of an acutely unwell patient1, whilst communication skills needed to assess a mentally unwell patient tends to be taught through role player2. 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. Summary of work 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. Summary of results 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’. Discussion and conclusions 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. Recommendations 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. References 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’, Medical Teacher27 : 10 – 28 McNaughton, N., Ravitz, P., Wadell, A., Hodges, B. (2008). ‘Psychiatric Education and Simulation: A Review of the Literature.’ The Canadian Journal of Psychiatry, 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 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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0360.011

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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Citations0
Published2019
Admission routes1
Has abstractyes

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