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Record W4402496886 · doi:10.3389/fresc.2024.1420498

The international classification of functioning, disability and health in clinical practice, research findings and their impact on training and education

2024· article· en· W4402496886 on OpenAlexaff
Liane Simon, Friederike Gölz, Olaf Schenk, Thorsten Bührmann, Mathias Kauff, Olaf Kraus de Camargo, Stefanus Snyman, George Lüers, Britta Wulfhorst

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthCurriculumMedical educationClinical PracticePsychologyFocus groupHealth professionalsMedicineNursingPedagogyHealth careRehabilitationPhysical therapySociologyPolitical science

Abstract

fetched live from OpenAlex

At the ICF Research Institute (at MSH Medical School Hamburg) multiprofessional experts collaborate on various research projects with a focus on bio-psycho-social health and education. Initially, the main goal was monitoring and evaluating the implementation of the International Classification of Functioning, Disability and Health (ICF) in clinical practice. Over time and based on the initial findings, the research group started developing new approaches to support training and education of health professionals in the use of the ICF. As a result, substantial changes have recently been made in the curriculum and structure of several courses to improve and expand interprofessional teaching at the MSH Medical School Hamburg (MSH). Furthermore, creative didactic approaches in combination with interprofessional education have been developed.

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)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.008
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.175
GPT teacher head0.599
Teacher spread0.424 · 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 designNot applicable
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

Citations4
Published2024
Admission routes1
Has abstractyes

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