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Record W2133874730 · doi:10.22230/jripe.2009v1n1a4

Effectiveness of a Faculty Development Program in Fostering Interprofessional Education Competencies

2009· article· en· W2133874730 on OpenAlexaffvenue
Debbie Kwan, Keegan K. Barker, Denyse Richardson, Susan Wagner, Zubin Austin

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterprofessional educationPsychological interventionRandomized controlled trialIntervention (counseling)Medical educationMedicineFaculty developmentProgram evaluationPsychologyNursingProfessional developmentHealth care

Abstract

fetched live from OpenAlex

AbstractBackground: To determine the effectiveness of a faculty development program offered to clinical faculty in fostering interprofessional education competencies.Methods and Findings: A pre-post randomized control group design was used in which only one of two cohorts of clinical faculty received an interprofessional educational intervention. Both cohorts then facilitated case-based interprofessional education sessions for student learners. A variety of outcome measures were used to assess differences between groups in terms of knowledge, skills, and attitudes related to interprofessional education and practice. No significant differences were noted between the control and intervention groups.Conclusions: The use of a pre-post randomized control group design to measure effectiveness of an educational intervention should be considered to demonstrate the impact of educational interventions.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.129
GPT teacher head0.596
Teacher spread0.467 · 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 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

Citations13
Published2009
Admission routes2
Has abstractyes

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