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Record W1016245867 · doi:10.22230/jripe.2012v2n3a96

Interprofessional Team Reasoning Framework as a Tool for Case Study Analysis with Health Professions Students: A Randomized Study

2012· article· en· W1016245867 on OpenAlexvenueno aff
Kathleen A. Packard, Hardeep Chelal, Anna Maio, Joy Doll, Jennifer Furze, Kathryn N. Huggett, Gail M. Jensen, Diane Jörgensen, Marlene Wilken, Yongyue Qi

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsRubricInterprofessional educationMedical educationMedicineRandomized controlled trialPsychologyPerceptionHealth carePedagogy

Abstract

fetched live from OpenAlex

Background: This pilot study evaluated the efficacy of the Interprofessional Team Reasoning Framework (IPTRF) to facilitate teaching and learning case studies with health professions students.Methods and Findings: Eighteen interprofessional students were randomized to teams of six and were videotaped while completing a case. Team 1 (control) received only the case; team 2 received the case plus framework; and team 3 received the case, framework, and was shown videotaped examples of interprofessional interactions. The primary endpoint was students’ perceptions of interprofessional skills as measured pre and post intervention using a modified Team Skills Scale. The secondary endpoint was student performance as assessed by blinded individuals using a standardized rubric. The results revealed that students’ perceptions of team skills were significantly improved in team 2 and team 3 but not team 1. Students’ performance of their case as assessed by blinded faculty was significantly better in team 3 compared with teams 1 and 2.Conclusions: In this study of six disciplines, the IPTRF, in combination with modeled examples of interprofessional communication, was an effective tool to teach skills necessary to workup a patient case, which included collaboration, communication, and values/ethics. As the landscape of interprofessional education evolves, tools like the IPTRF will facilitate incorporation of these skills into health professions education.

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)0.045
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.004
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 head0.081
GPT teacher head0.622
Teacher spread0.541 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations22
Published2012
Admission routes1
Has abstractyes

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