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Record W2138312350 · doi:10.1080/13561820500498154

The Interprofessional Rural Program of British Columbia (IRP<i>bc</i>)

2006· article· en· W2138312350 on OpenAlexaffabout
Grant Charles, Lesley Bainbridge, Kathy Copeman-Stewart, Shelley Tiffin Art, Rosemin Kassam

Bibliographic record

VenueJournal of Interprofessional Care · 2006
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsBritish Columbia Institute of TechnologyUniversity of British Columbia
Fundersnot available
KeywordsMandateInterprofessional educationCompetence (human resources)Medical educationSocial workRural areaNursingMedicineWork (physics)PsychologyHealth carePolitical science

Abstract

fetched live from OpenAlex

The Interprofessional Rural Program of British Columbia IRPbc was established in 2003 as an important first step for the Province of British Columbia, Canada, in creating a collaborative interprofessional education initiative that engages numerous communities, health authorities and post-secondary institutions in working toward a common goal. Designed to foster interprofessional education and promote rural recruitment of health professionals, the program places teams of students from a number of health professional programs into rural and remote British Columbia communities. In addition to meeting their discipline specific learning objectives, the student teams are provided with the opportunity to experience the challenges of rural life and practice and advance their interprofessional competence. To date, 62 students have participated in the program from nursing, social work, medicine, physical therapy, occupational therapy, pharmaceutical sciences, speech language pathology, audiology, laboratory technology, and counseling psychology. While not without numerous struggles and challenges, IRPbc has been successful in meeting the program mandate. It has also had a number of positive outcomes not anticipated at the time the program was established.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.002
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 head0.009
GPT teacher head0.384
Teacher spread0.376 · 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

Citations38
Published2006
Admission routes2
Has abstractyes

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