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Record W2412186094 · doi:10.3109/13561820.2016.1169262

Introducing the individual Teamwork Observation and Feedback Tool (iTOFT): Development and description of a new interprofessional teamwork measure

2016· article· en· W2412186094 on OpenAlexaff
Jill Thistlethwaite, Kathy Dallest, Roger Dunston, Chris Roberts, Diann Eley, Fiona Bogossian, Dawn Forman, Lesley Bainbridge, Donna Drynan, Sue Fyfe

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British Columbia
FundersOffice for Learning and Teaching
KeywordsTeamworkFormative assessmentDelphi methodDelphiMedical educationAccreditationPsychologyTask (project management)Patient safetyComputer scienceHealth careMedicinePedagogyEngineeringPolitical science

Abstract

fetched live from OpenAlex

The individual Teamwork Observation and Feedback Tool (iTOFT) was devised by a consortium of seven universities in recognition of the need for a means of observing and giving feedback to individual learners undertaking an interprofessional teamwork task. It was developed through a literature review of the existing teamwork assessment tools, a discussion of accreditation standards for the health professions, Delphi consultation and field-testing with an emphasis on its feasibility and acceptability for formative assessment. There are two versions: the Basic tool is for use with students who have little clinical teamwork experience and lists 11 observable behaviours under two headings: 'shared decision making' and 'working in a team'. The Advanced version is for senior students and junior health professionals and has 10 observable behaviours under four headings: 'shared decision making', 'working in a team', 'leadership', and 'patient safety'. Both versions include a comprehensive scale and item descriptors. Further testing is required to focus on its validity and educational impact.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

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

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 head0.052
GPT teacher head0.363
Teacher spread0.311 · 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.

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

Citations79
Published2016
Admission routes1
Has abstractyes

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