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2017· article· en· W2763211001 on OpenAlexaboutno aff
Gina Shaw

Bibliographic record

VenueEmergency Medicine News · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

role play: role playA woman named Leah is found in a field acting erratically after having either fallen or jumped from a high place while hiking. Emergency personnel bring her from the scene to the hospital, where she is seen by an emergency physician. Leah is not every doctor's dream patient—she's abrasive and combative. The emergency physician is struggling to assess her condition and determine whether her agitation is simply her personality or a manifestation of her injuries. She wants to leave the hospital against medical advice. Does the emergency physician let her go or try harder to get her to stay until she can be fully worked up? That's the scenario that played out over the course of three days of dramatic immersive experiential role play sessions at the Social Media and Critical Care (SMACC) meeting in Berlin this year. Leah was played by Renee Lim, MD, an Australian actress and television presenter who is also a practicing physician and the director of program development for the Pam McLean Centre, a clinical communication skills training facility based at Sydney Medical School Northern at Royal North Shore Hospital. Dr. Lim as Leah was assessed in the field by a Sydney emergency physician and the simulation lead of the Greater Sydney Area Helicopter Emergency Service, Clare Richmond, MD, on day one. She was treated by emergency physician Chris Hicks, MD, the trauma team leader at St. Michael's Hospital in Toronto, Ontario, Canada, on the next day. After having been discharged by an overwhelmed and irritated Dr. Hicks on day three, Leah was found in the intensive care unit with an irreversible brain injury, under the care of Sydney intensive care specialist Jon Gatward, MD. “What the organizers wanted was a large-scale, three-day simulation exercise that looked at a patient encounter over prehospital, emergency, and ICU care,” explained Dr. Hicks. The intense sessions highlighted the potential for live, immersive role play with trained actors to help medical students, residents, and even seasoned emergency medicine professionals learn communication skills more comprehensively and profoundly than didactic education, simulations with a computer or a mannequin, or even the classic standardized patient encounter. It's an approach that Dr. Hicks has begun taking with his own residents by hiring actors through the University of Toronto's standardized patient program. The simulation curriculum has between 15 and 20 slots throughout the year, usually on academic half-days. Dr. Hicks' 50 residents are broken up into small groups and distributed throughout the sessions, so each gets about five simulation sessions per year. “Our actors can do anything from a standard OSCE (objective structured clinical examination) ‘my knee hurts on the outside’ scenario to complex characters with backgrounds, costumes, the whole nine yards,” said Dr. Hicks. Two actors work consistently with the EM residency, he said. “In the more complex scenarios, they have a character and a purpose but not a script or a set way of responding. We often use them, much as we demonstrated at SMACC, as part of our psychological skills training program.” Complex Communications In one typical encounter, one of these actors is brought to the trauma room playing an extremely upset stabbing victim. “He comes in from triage with a knife sticking out of his shirt and a lot of blood on him,” Dr. Hicks said. “We assess everything from how he gets assessed at triage and registered, how he gets worked up, and the trauma team and surgical response. Our residents get feedback from the actor or from the actor's character about their experience coming in as a stab victim to St. Michael's Hospital.” Teaching complex communication in medicine—like breaking the news of a fatal diagnosis or dealing with your own emotional response to a combative patient—is not about learning a specific skill like making eye contact or introducing yourself properly or something more difficult like a surgical technique. “The standard approach to teaching medical students and residents about things like breaking bad news assumes that the person will have no reaction and no issues other than sadness,” Dr. Lim said. “For the sake of simplicity and ease of learning, it's easier to take that approach. If I say these things, the patient will be sad; and if I say these next things, the patient will be slightly less sad, and I can help them through being sad. But no one who gets the news that they're dying of cancer is just sad. They have a whole complex life behind them. They're scared for their child. They're angry at a doctor who missed a diagnosis. They're worried about bills. But there's no way you can create an environment to repeatedly practice dealing with exactly that. It's different every time you do it.” Instead, the Pam McLean Centre's immersive simulation programs aim to instill self-reflection and behavioral awareness in the clinician. “It's less about what you should do and these are the rules to follow, and more about developing ways of thinking and assessing situations to create new rules for each conversation,” she said. “We can get across that there are some rules that apply in the majority of cases while still being constantly aware enough to recognize when those rules aren't working.” To make everything as real as possible, the McLean Centre uses paid actors recruited through a rigorous process and given a five-page back story that incorporates everything from where they were born and who their best friend is to why they divorced their wife and what their favorite food is. “All these individual components create an entire human being, which is why you never get the same response twice,” Dr. Lim said. Dr. Richmond, who led day one of the SMACC simulation experience, said her program in Sydney doesn't even own a mechanized mannequin. “We do quite a lot of immersive simulation; sometimes we use simple Crash Kellys, but predominantly we use human volunteers,” she said. “We give them a background, a back story, what we want the patient to be like, and what we expect the learning objectives to be.” High-tech simulation centers featuring everything from advanced mannequins to the latest in 3D virtual reality are becoming more and more common in medical education, and they excel in teaching skills like managing a difficult airway or inserting central venous catheters. But extensive, immersive communication-based simulation programs involving trained actors are still relatively rare in emergency medicine and medicine in general. Dr. Hicks said he believes his program is the only one of its kind in Canada. They may grow, however, as experiences like the SMACC session demonstrated. Dr. Lim said you need the right tool for the right thing, and simulation like this is an important complement to clinical skills simulation training. It helps clinicians learn that it's OK to get it wrong in communications sometimes,” she said. “If you're never getting it wrong, you're probably not getting it right either. This kind of simulation allows you to continually reassess yourself and see whether your patient communications are working through multiple lenses: yours, the patients', and your colleagues'.” Share this article on Twitter and Facebook. Access the links in EMN by reading this on our website or in our free iPad app, both available at www.EM-News.com. Comments? Write to us at [email protected].

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.243
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.7570.646

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 head0.252
GPT teacher head0.493
Teacher spread0.241 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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