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Record W1145105478

Work-based assessment of teamwork: an inter professional approach. Final Report

2015· article· en· W1145105478 on OpenAlexaboutno aff
Jill Thistlethwaite, Kathy Dallest, Lesley Bainbridge, Fiona Bogossian, David Boud, Roger Dunston, Diann Eley

Bibliographic record

VenueUSC Research Bank (University of the Sunshine Coast) · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersUniversity of Technology SydneyOffice for Learning and TeachingAustralian Government
KeywordsTeamworkFormative assessmentGeneral partnershipMedical educationCurriculumConstructiveHealth careWork (physics)Health professionalsKnowledge managementPsychologyEngineeringMedicineComputer sciencePedagogyPolitical scienceProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Final report from a project that addressed the nationally recognized need to develop and deliver a robust package of work-based assessment (WBA) tools for health professional students in diverse clinical settings as a means of testing their teamwork competencies and interprofessional collaboration in the workplace. Teamwork competencies are included as both graduate attributes in general and in healthcare curricula in particular but are rarely assessed. The project provided an assessment framework that not only assesses students individually as team members but also considers a team as a single entity with a view to enhancing team performance. This partnership of four Australian and one Canadian university reviewed and evaluated existing WBA tools for teamwork across the health professions and developed a framework and instruments for formative assessment that are valid, reliable, feasible and suitable for the provision of timely and constructive feedback to students.

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)0.027
metaresearch head score (Gemma)0.032
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: none
Teacher disagreement score0.027
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.180
GPT teacher head0.432
Teacher spread0.252 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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