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Record W1517551829 · doi:10.22230/jripe.2013v3n1a89

A Team Observed Structured Clinical Encounter (TOSCE) for Pre-Licensure Learners in Maternity Care: A Short Report on the Development of an Assessment Tool for Collaboration

2013· article· en· W1517551829 on OpenAlexaffvenueabout
Beth Murray‐Davis, Patricia Solomon, Anne Malott, Valerie Mueller, Elizabeth Shaw, Kelly Dore, Sheri Burns

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSummative assessmentFormative assessmentMedical educationMedicineHealth careProcess (computing)Interprofessional educationLicensureDelphi methodPsychologyPedagogyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Background: Despite the support for Interprofessional Education (IPE) among policymakers, educators and professional regulating bodies, the research literature is limited with respect to the evaluation of effective assessment strategies. This short report outlines the development of a Team Observed Structured Clinical Encounter (TOSCE), which brings together learners from three health professions involved in primary care obstetrics-family physicians, midwives, and obstetricians-as a strategy for assessing collaborative competencies.Methods: An interprofessional research team was brought together to develop and implement the TOSCE. The process by which the team generated TOSCE scenario stations is outlined, including the consensus-building process, based on a modified Delphi technique, to include expert input from others in the field of practice.Findings: The scenarios developed by the research team for the TOSCE are highlighted including the assessment criteria, based on the Canadian InterprofessionalHealth Collaborative's National Competency Framework.Conclusions: The TOSCE is an emerging and innovative learning tool that encourages the development of essential collaborative competencies. The process of developing a TOSCE outlined in this report offers an affordable, streamlined approach that could be used by educators in many disciplines as a summative or formative assessment strategy.

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)0.013
metaresearch head score (Gemma)0.007
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 score0.284
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.154
GPT teacher head0.619
Teacher spread0.465 · 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

Citations12
Published2013
Admission routes3
Has abstractyes

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