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Record W2611938647 · doi:10.25011/cim.v30i4.2768

8. Modelling and teaching collaboration: The interprofessional education in geriatric care project

2007· article· en· W2611938647 on OpenAlexaffvenueabout
Ruby Grymonpre, Elizabeth M. Boustcha, Michelle Nelson, Ann Booth, Amy De Jaeger, Fiona Jensen, Theresa Sullivan, Jenneth Swinamer, Leah Weinberg

Bibliographic record

VenueClinical and investigative medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPreceptorMedical educationInterprofessional educationMedicinePharmacyExperiential learningFlexibility (engineering)NursingCore competencyHealth carePsychologyPedagogy

Abstract

fetched live from OpenAlex

The Interprofessional Education in Geriatric Care (IEGC) project is an Interprofessional Education for Collaborative Patient-centred Practice Initiative (Health Canada) funded project that was launched in June 2005 and ends March 2008. The goal of the IEGC project is to develop a sustainable clinical experience that models and teaches the core concepts of patient centred collaboration (goal directedness, leadership, flexibility, team dynamics, conflict resolution, communication, and disciplinary articulation) which directly reflect the enabling competencies of the “Collaborator Role” outlined in the 2005 CanMEDS Framework. The primary learners are Internal Medicine/Family Medicine residents and senior students in nursing, occupational therapy, physiotherapy, and pharmacy. The interprofessional education occurs at three geriatric day hospitals where collaborative patient-centred care of community dwelling older clients is the standard of practice, and clinicians traditionally accept students for clinical placements. The IEGC project identified instances that learners from two or more disciplines were at Day Hospital simultaneously for approximately three weeks. During this time, experiential learning activities emphasizing the skills needed for effective collaboration were added to typical preceptor based teaching. Feedback early in the project from students and preceptors indicated that the original design, (practice simulations and didactic small group sessions), were not meeting clinical learning objectives. In response, the clinical teams initiated team led discussions regarding core concepts and students were given the opportunity to assess and develop care plans for “student team” designated patients. In addition structured team observations, directed readings, self-reflective exercises and “ice-breakers” are utilized. Informal feedback from participants has suggested that this is more effective, and relevant way to teach patient centred collaborative practice. The IEGC project has extensive research and evaluation methods based on the JET’s modification of Kirkpatrick’s Model of Educational Outcomes which will assess the effectiveness and viability of the IEGC educational approach. The Interprofessional Joint Evaluation Team. (2002). A Critical Review of Evaluations of Interprofessional Education. http://www.health.heacademy.ac.uk/publications/ occasionalpaper/occasionalpaper02.pdf/view. Accessed September 9, 2005. Frank JR, Jabbour M, et al. Eds. Report of the CanMEDS Phase IV Working Groups. Ottawa: The Royal College of Physicians and Surgeons of Canada. March, 2005.

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)0.013
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0050.003
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.150
GPT teacher head0.518
Teacher spread0.367 · 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

Citations0
Published2007
Admission routes3
Has abstractyes

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