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Record W2001059664 · doi:10.1080/01421590701769548

Interprofessional resuscitation rounds: a teamwork approach to ACLS education

2008· article· en· W2001059664 on OpenAlexaff
Jeffrey Damon Dagnone, Robert McGraw, Cheryl Pulling, Ann Patteson

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsQueen's University
Fundersnot available
KeywordsTeamworkSession (web analytics)SpecialtyResuscitationMedical educationMedicineAdvanced cardiac life supportCurriculumNursingPsychologyCardiopulmonary resuscitationFamily medicineEmergency medicinePedagogy

Abstract

fetched live from OpenAlex

PURPOSE: We developed and implemented a series of interprofessional resuscitation rounds targeting fourth year nursing and medical students, and junior residents from a variety of specialty programs. METHODS: Each two hour session was conducted in our patient simulation lab, and was held weekly during the academic year. Students were given specific instruction on the roles and responsibilities of resuscitation team members, and then teams of five worked through pre-defined Advanced Cardiac Life Support (ACLS) scenarios on a high fidelity patient simulator. At the end of each session students completed an anonymous evaluation of the program via a standardized questionnaire using Likert rating scales. RESULTS: A total of 222 evaluations (101 nursing students, 42 medical students, and 79 junior residents) were submitted from October 2005 to April 2006. Mean scores reflected a strong consensus that these rounds were valuable for their training, provided a vehicle for understanding team roles in resuscitation, and that these rounds should be mandatory for all medical and nursing trainees. Participants also expressed a desire for additional interprofessional training. CONCLUSION: Despite challenges inherent in teaching a diverse group of learners, these interprofessional resuscitation rounds were rated highly by nursing and medical trainees as valuable learning experiences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.398
Teacher spread0.342 · 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 teacher head, not a consensus.

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

Citations53
Published2008
Admission routes1
Has abstractyes

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