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Record W2763700901 · doi:10.7710/2159-1253.1131

An Innovative, Arts-Based Approach to Interprofessional Education

2017· article· en· W2763700901 on OpenAlexaboutno aff
Sheri Price, Meaghan Sim, Megan Aston, Christine Awad, Sara Kirk

Bibliographic record

VenueHealth and Interprofessional Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsInterprofessional educationSociologyMedical educationVisual artsPolitical scienceMedicineArtHealth care

Abstract

fetched live from OpenAlex

Obesity is a global health concern that is challenging to address at the health system level. This is partly because health professionals perceive themselves to be poorly equipped to effectively handle weight management issues. Compounding this are the biases held by health professionals towards clients living with excess weight. This project aimed to address these biases among health professionals through the use of live, dramatic arts as a pedagogical tool to disseminate findings from an original research study that explored multiple health system perspectives on weight management. Using an interprofessional learning format, the research team facilitated four, interprofessional education (IPE) workshops in universities across Atlantic Canada with health professional students and their faculty. Post-workshop evaluations indicate that the workshop was well received; the live, dramatic presentation of professional-client perspectives on obesity management was effective for not only facilitating understanding about the original research findings, but in provoking thought about issues of weight management and providing an opportunity for attendees to engage in interprofessional learning. The majority of participants perceived that this workshop would positively benefit them in their future work as health 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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.083
GPT teacher head0.566
Teacher spread0.483 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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