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Record W2947186784 · doi:10.1093/pch/pxz066.054

55 Outside the comfort zone: Evaluation of a simulation-based curriculum in managing agitated patients for paediatric residents

2019· article· en· W2947186784 on OpenAlexaff
Lindsay Fleming, Chetana Kulkarni, Sharon Lorber, Katherine Hick

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCurriculumMedicineMedical educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

Given the prevalence of mental health comorbidity in the paediatric population, it is important that paediatricians/paediatric trainees possess competence in the management of acute psychiatric emergencies, including agitation. Experiential learning through simulation is used for safety training when high-risk decisions must be made safely and rapidly. Simulation-based training in the management of acutely agitated patients has been studied in the context of psychiatric training programs; however, limited studies exist to inform effective methods of training paediatric residents in the management of agitation. This study assessed a simulation-based workshop on the knowledge, competence and confidence of paediatric trainees in the management of agitated patients compared to a didactic lecture or no formal educational intervention. This was a prospective comparative cohort study. Paediatric residents were divided among three study groups: Group 1 - a 1-hour didactic lecture on agitation management; Group 2 - a simulation-based workshop on managing the agitated patient; and Group 3 -no intervention. Confidence and knowledge were assessed in groups 1 and 2 using a pre- and post- intervention self-efficacy questionnaire and a clinical vignette. All three groups completed an agitated patient station in the 2018 in-training Objective Structured Clinical Examination (OSCE) assessment. Univariate analysis was completed on the pre- and post- intervention self-efficacy questionnaires, and clinical vignette scores were analyzed using a t-test. Analysis of variance was used to compare OSCE scores between groups. A subgroup analysis was performed to assess OSCE scores by postgraduate year (PGY). Simulation-based workshop participants performed better in the OSCE scenario as demonstrated by their OSCE score (mean 81.7%, CI: 75.1–88.3) compared to those from groups 1 (mean 74.6%, CI: 71.4–77.8) and 3 (mean 71.6%, CI: 69.2–74.0). The most significant difference was present between groups 2 and 3 (p=0.0055), whereas differences in the means between groups 1 and 2 were not as prominent (p=0.0577). No difference in mean OSCE scores was found between groups 1 and 3 (p= 0.1424). Subgroup analysis of OSCE scores by PGY of training did not demonstrate a statistically significant difference (p=0.0615). Scores for this scenario did not improve with increased level of training, demonstrating a persistent knowledge gap amongst trainees. Simulation-based learning may be an effective educational strategy for paediatric residents to acquire skills in managing acute agitation.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.320
Teacher spread0.305 · 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 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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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