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Record W2027335148 · doi:10.3109/13561820.2014.890923

Collaboration behind-the-scenes: key to effective interprofessional education

2014· article· en· W2027335148 on OpenAlexafffundabout
Diane MacKenzie, Shelley Doucet, Susan Nasser, Anne L. Godden-Webster, Cynthia Andrews, George Kephart

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCommunity Sector Council Newfoundland and LabradorUniversity of New BrunswickDalhousie University
FundersDalhousie University
KeywordsInterprofessional educationMedical educationVariety (cybernetics)Social workUnderpinningLicensureHealth careWork (physics)PsychologyNursingMedicinePolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

A variety of stakeholders, including students, faculty, educational institutions and the broader health care and social service communities, work behind-the-scenes to support interprofessional education initiatives. While program designers are faced with multiple challenges associated with implementing and sustaining such programs, little has been written about how program designers practice the interprofessional competencies that are expected of students. This brief report describes the backstage collaboration underpinning the Dalhousie Health Mentors Program, a large and complex pre-licensure interprofessional experience connecting student teams with community volunteer mentors who have chronic conditions to learn about interprofessional collaboration and patient/client-centered care. Based on our experiences, we suggest that just as students are required to reflect on collaborative processes, interprofessional program designers should examine the ways in which they work together and take into consideration the impact this has on the delivery of the educational experience.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.424
Teacher spread0.415 · 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

Citations14
Published2014
Admission routes3
Has abstractyes

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