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Record W2089448921 · doi:10.1080/13561820802028410

Achieving social accountability through interprofessional collaboration: The Canadian medical schools experience

2008· article· en· W2089448921 on OpenAlexaffabout
Kendall Ho, Denise Buote, Sandra Jarvis-Selinger, Helen Novak Lauscher, Luke Ferdinands, Jean Parboosingh, Sue Maskill, Robert Woollard

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsCanmore Museum and Geoscience CentreUniversity of British Columbia
Fundersnot available
KeywordsAccountabilityCornerstoneInterprofessional educationSocial accountingContext (archaeology)Health carePublic relationsSocial workAction (physics)Political scienceMedical educationMedicineBusiness

Abstract

fetched live from OpenAlex

Social accountability in the health professions is increasingly recognized as a necessary foundation for delivering effective healthcare. Inter- and intra-professional collaboration is critical to the process in order to transform intent into action. This article outlines the three-year program undertaken by a national collaboration among all 17 Canadian medical schools and their partners as they engaged in a journey leading to the incorporation of social accountability in an interprofessional context as the cornerstone of healthcare education and practice. An overview of the various dimensions of this project is discussed in order to shed light on how a national initiative in collaboration with local initiatives can synergistically work toward a common goal. Successes and challenges in working on a national level are reviewed with implications for future directions for interprofessional collaboration in healthcare based upon principles and values of social accountability.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.410
Teacher spread0.382 · 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 designObservational
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

Citations15
Published2008
Admission routes2
Has abstractyes

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