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Record W1952416052 · doi:10.46743/1540-580x/2004.1049

Changing Attitudes - Health Sciences Students Working Together.

2004· article· en· W1952416052 on OpenAlexaffabout
Elizabeth Taylor, David A. Cook, Rosemarie Cunnigham, Sharla King, Jan Pimlott

Bibliographic record

VenueInternet Journal of Allied Health Sciences and Practice · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedical educationLimitingPsychologyHealth professionalsFunction (biology)Process (computing)Health professionsPedagogyHealth careMedicinePolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Is it possible to alter limiting stereotypic attitudes of health professionals toward each other? Perhaps a first step might be an undergraduate interdisciplinary course that brings students from different faculties together to work on scenarios of common interest? The Inter-professional Health Development, Education & Activities Group (IHDEA) at the University of Alberta believe that their innovative INTD 410 course addresses the goal of changing attitudes. Over a five-week period, more than 700 students attend this required course. They are supported by some fifty facilitators who are drawn from the community and from six different university faculties. Students interact in small interdisciplinary teams and in the process deepen their knowledge of the role of each health professional, and come to a greater understanding of the contributions of their own discipline to the team. Data gathered suggest that the course cultivates respect among the professions and that students feel better able to function within the health team. This paper describes how the course was developed.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.106
GPT teacher head0.499
Teacher spread0.393 · 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 designOther design
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

Citations17
Published2004
Admission routes2
Has abstractyes

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