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Record W2096386134 · doi:10.1080/13561820701753969

Skills integration in a simulated and interprofessional environment: An innovative undergraduate applied health curriculum

2008· article· en· W2096386134 on OpenAlexaff
Karim S. Bandali, Kathryn Parker, Michelle Mummery, Mary Preece

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMichener Institute
Fundersnot available
KeywordsPreparednessCurriculumInterprofessional educationMedical educationHealth careCore competencyMultidisciplinary approachMedicineConstruct (python library)NursingPsychologyPedagogySociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The objective of our study was to propose an innovative applied health undergraduate curriculum model that uses simulation and interprofessional education to facilitate students' integration of both technical and "humanistic" core skills. The model incorporates assessment of student readiness for clinical education and readiness for professional practice in a collaborative, team-based, patient-centred environment. Improving the education of health care professionals is a critical contributor to ultimately improving patient care and outcomes. A review of the current models in health sciences education reveals a scarcity of clinical placements, concerns over students' preparedness for clinical education, and profession-specific delivery of health care education which fundamentally lacks collaboration and communication amongst professions. These educational shortcomings ultimately impact the delivery and efficacy of health care. Construct validation of clinical readiness will continue through primary research at The Michener Institute for Applied Health Sciences. As the new educational model is implemented, its impact will be assessed and documented using specific outcomes measurements. Appropriate modifications to the model will be made to ensure improvement and further applicability to an undergraduate medical curriculum.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.359
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.

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

Citations46
Published2008
Admission routes1
Has abstractyes

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