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Record W2606482289 · doi:10.1111/medu.13331

Articulating the ideal: 50 years of interprofessional collaboration in <i>Medical Education</i>

2017· article· en· W2606482289 on OpenAlexafffund
Elise Paradis, Mandy Pipher, Carrie Cartmill, J. Cristian Rangel, Cynthia Whitehead

Bibliographic record

VenueMedical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsOntario College of Art and DesignToronto Arts FoundationThe Wilson CentreWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchAssociation for the Study of Medical EducationSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsUniversity Health Network
KeywordsContext (archaeology)Medical terminologyPsychologyMedical educationTerminologySample (material)Health careSociologyPedagogyMedicineNursingPolitical science

Abstract

fetched live from OpenAlex

CONTEXT: Health care delivery and the education of clinicians have changed immensely since the creation of the journal Medical Education. In this project, we seek to answer the following three questions: How has the concept of collaboration changed over the past 50 years in Medical Education? Have the participants involved in collaboration shifted over time? Has the idea of collaboration itself been transformed over the past 50 years? METHODS: Starting from a constructionist view of scientific discourse, we used directed content analysis to sample, code and analyse 144 collaboration-related articles over the 50-year life span of Medical Education. We developed an analytical framework to identify the key components of varying articulations of 'collaboration', with a focus on shifts in language and terminology over time. Our sample was drawn from an archive of 1221 articles developed to celebrate the 50th anniversary of Medical Education. RESULTS: Interprofessional collaboration is conceptualised in three primary ways throughout our sample: as a psychometric property; as tasks or activities, and, more recently, as 'togetherness'. The first conceptualisation articulates collaboration as involving knowledge or skills that are teachable to individuals, the second as involving the education of teams to engage in structured meetings or task distribution, and the third as the building of networks of individuals who learn to form team identities. The 'leader' of collaboration is typically conceptualised as the doctor, who is consistently articulated by authors as the active agent of collaborative care. Other clinicians and students of other professions are, as the wording in this sentence suggests, usually positioned as 'others', and thus as more passive participants in, or even observers of, 'collaboration'. CONCLUSIONS: In order to meet goals of meaningful collaboration leading to higher-quality care, it behoves us as a community of educators and researchers to heed the ways in which we teach, think and write about interprofessional collaboration, interrogating our own language and assumptions that may be betraying and reproducing harmful care hierarchies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.060
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.005
Science and technology studies0.0110.037
Scholarly communication0.0260.038
Open science0.0020.015
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.476
Teacher spread0.459 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations38
Published2017
Admission routes2
Has abstractyes

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