Management of Medical Emergencies on Psychiatry Inpatient Floors: A Novel Simulation-Based Curriculum for Psychiatry Residents
Bibliographic record
Abstract
Introduction For most psychiatry residents, training in managing the early phases of acute medical emergencies is limited to exposure during medical school and junior off-service rotations. Patients with psychiatric illnesses, however, often have higher rates of medical comorbidities than their age-matched controls. We developed a half-day medical refresher and simulation curriculum to improve the confidence and efficacy with which psychiatry residents can manage medical emergencies on inpatient floors. Methods Based upon a detailed needs assessment conducted with psychiatry residents at various stages of their training, we developed nine clinical scenarios that could be encountered by psychiatry residents managing inpatient units. These included: shortness of breath; sepsis, acute coronary syndrome; cardiac arrest; seizure; overdose; laceration; asphyxiation from hanging; and smoke inhalation. Training sessions included basic skills stations (such as how to obtain vitals or perform bag mask ventilation). Participants completed anonymous surveys prior to the sessions to provide information on their location and level of residency training and their experience with managing medical emergencies. Pre- and post-training surveys assessed participants' confidence levels with various basic procedural skills and general emergency management. Focus groups were performed to obtain qualitative data about participants' experiences and opinions about the simulation scenarios and training session. Results Level of experience varied among participants with respect to exposure to medical emergencies. Most respondents reported a strong desire to have training sessions on these topics. Reported confidence levels increased across multiple domains. Further, residents in general expressed high satisfaction with the scenarios and skills sessions. Residents also identified topics for further medical simulations. Discussion We developed a half-day simulation curriculum focused on essential emergency skills and initial management of a variety of plausible scenarios that could be encountered on a psychiatry inpatient unit. This curriculum could be easily integrated and regularly run as part of any psychiatry residency program. Most importantly, this program may greatly enhance the confidence and efficacy of psychiatry residents faced with managing the initial phase of medical emergencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".