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Record W1982513923 · doi:10.3109/0142159x.2012.642829

Innovations in applied health: Evaluating a simulation-enhanced, interprofessional curriculum

2012· article· en· W1982513923 on OpenAlexaff
Karim S. Bandali, Robert C. Craig, Amitai Ziv

Bibliographic record

VenueMedical Teacher · 2012
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMichener Institute
Fundersnot available
KeywordsPracticumPreparednessCurriculumMedical educationInterprofessional educationHealth careIntervention (counseling)MedicineFocus groupPsychologyNursingPedagogySociology

Abstract

fetched live from OpenAlex

BACKGROUND: In response to current trends in healthcare education, teachers at the Michener Institute for Applied Health Sciences implemented a New Curriculum Model (NCM) in 2006, building a curriculum to better transition students from didactic to clinical education. Through the implementation of interprofessional education and simulated clinical scenarios, educators created a setting to develop, contextualize and apply students' skills before entry to the clinical environment. AIMS: In this pilot study, researchers assessed the impact of the NCM intervention on student preparedness for clinical practicum. METHODS: A mixed-methods evaluation was conducted, collecting survey assessments and qualitative focus group feedback from clinical educators and students. RESULTS: Clinical educators identified Michener NCM students to be significantly better prepared for clinical practicum when compared to previous cohorts (p < 0.05%). Students also noted significant improvements as implementation issues were resolved from years one to two of the NCM. CONCLUSIONS: The infusion of simulation and interprofessional education into Michener's applied health curricula resulted in a significant improvement in clinical preparedness. The Michener NCM bridged the gap previously separating didactic education and clinical practice, transitioning applied health students from trained technicians to more complete health care professionals.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.323
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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)0.0080.000

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 head0.094
GPT teacher head0.493
Teacher spread0.399 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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