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Record W1967378757 · doi:10.1080/13561820802565619

Interprofessional learning in the trenches: Fostering collective capability

2009· article· en· W1967378757 on OpenAlexafffund
Hassan Soubhi, Nicole Rege Colet, John Gilbert, Paule Lebel, Robert Thivierge, Catherine Hudon, Martin Fortin

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversity of British ColumbiaUniversité de MontréalUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsKnowledge managementInterdependenceValue (mathematics)PsychologyOrganizational learningResource (disambiguation)Social workWork (physics)SociologyComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The greatest resource for improving interprofessional learning and practice is the knowledge, wisdom, and energy of professionals who adapt to challenging situations in their everyday work. We call collective capability the ability of a group of professionals to balance two interdependent levels of organization of practice: what professionals know and what they do collectively over time. Organizing what professionals know links the relational value--caring for patients--to the knowledge value of practice. Organizing what professionals do includes human and organizational factors that facilitate collective work and learning: technical skills for care delivery, institutional support, and a complex mix of emotional, ethical and moral factors involved in social decision-making. Performance gaps can result from a lack of an integrated knowledge framework or from a disembodied knowledge that is not anchored in practice. Opportunities for continuous learning can be seized by documenting the source of the performance gap, and providing the relevant resources to establish the balance between the organization of knowledge and the organization of work.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.443
Teacher spread0.404 · 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 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

Citations36
Published2009
Admission routes2
Has abstractyes

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