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Record W1757368481

Creation, implementation and evaluation of an in situ simulation based interprofessional pediatric critical care curriculum

2013· article· en· W1757368481 on OpenAlexaffabout
Mélissa Langevin, Tracey Faulkner, Carolina Escudero, Anne Drover

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDebriefingCurriculumMedical educationHealth carePsychologyMedicineNursingPedagogy
DOInot available

Abstract

fetched live from OpenAlex

Pediatric critical events have become an infrequent occurrence in many pediatric centers due to improved child health practices. The ability to successfully care for a critically ill child depends on knowledge and skill but most importantly on a well-functioning team. It has been recognized that communication and collaboration between team members is an important competency for all health professions to obtain. Developing a well?functioning team may depend on the ability to learn in an interprofessional environment. This project aimed to develop, implement and evaluate an In Situ Simulation Based Interprofessional Pediatric Critical Care Curriculum. A review of the steps in the curriculum design and scenario creation is discussed. Canadian Pediatric Royal College Objectives, Crisis Resource Management Principles, the 2010 Heart & Stroke/PALS guidelines were used to create 18 Core Scenarios & 2 Trauma Scenarios. Factors taken into account in the development of the program were: Environment, Logistics, Participants, Realism, and Evaluations. Factors are discussed regarding engaging the participants. Results of the evaluations on debriefing and knowledge translation reveal that: 93% of participants reported an overall positive experience with non-resident members being significantly more likely (p=0.024) to have perceived the debriefing as a positive, 95% felt the feedback was useful for their learning, 81% felt they actively contributed to the scenario, 87% were able to integrate previous feedback in subsequent sessions. The benefit of an In-Situ program is that it is possible to highlight System failures. System Changes implemented secondary to the program were the following: Awareness raised regarding hospital airway protocol, Medication delivery issues highlighted: Providing infusion protocols in the ED, knowledge of location of infrequently used medications (prostaglandin), better understanding of scope of practice limits for IV PUSH meds, Recognition that residents require more training in intra-osseous placement and defibrillator use, Residents and ED nurses given in?service for gaps in skill. The challenges of an interdisciplinary In-situ simulation program were found to be: ensuring protected time for nurses and RTs, building a critical mass of instructors/debriefers, providing appropriate, timely orientation to all users and developing a bank of scenarios. Overall, it was found that it is possible to develop and implement an interprofessional, in-situ simulation program that is useful for all learners and can contribute to knowledge gain and skill development.

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)0.011
metaresearch head score (Gemma)0.013
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
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 head0.019
GPT teacher head0.369
Teacher spread0.350 · 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
Published2013
Admission routes2
Has abstractyes

Explore more

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