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Record W4408644758 · doi:10.1080/02640414.2025.2477393

International consensus on the definition of functional training: Modified e-Delphi method

2025· article· en· W4408644758 on OpenAlexaff
Hugo V. Pereira, Diogo Teixeira, James Fisher, Steven J. Fleck, Eric R. Helms, Bernardo Neme Ide, Míkel Izquierdo, Anders Nedergaard, Stuart M. Philips, Ronei Silveira Pinto, Daniel L. Plotkin, Anthony N. Turner, Brad J. Schöenfeld

Bibliographic record

VenueJournal of Sports Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDelphiComputer scienceConsensus conferenceLibrary science

Abstract

fetched live from OpenAlex

The inconsistency and disparities in existing functional training definitions have led to confusion when explaining the concept and its potential. The wide range of interpretations suggests that any training approach could be deemed functional, thereby diminishing the significance of the term and significantly limiting its understanding and application. Thus, this paper aimed to develop the first consensual definition of functional training using an international e-Delphi method. From a panel of 31 experts initially selected, 13 participated in the consensus. The panel presented the following definition: ‘Functional training is a physical interventional approach that contributes to the enhancement of human performance, according to individual goals, in sports, daily life, rehabilitation, or fitness, and takes into consideration the specificity of the task and the unique responsiveness of each individual’. However, redundancy of the functional training definition emerged as a relevant consideration for this conceptual and methodological advancement, and a proposal to avoid the distinction between functional training and the general concept of training was presented (i.e., no real use of functional training as a concept). It was proposed that a training program or regimen could be analyzed based on a continuum of functionality, which could support further developments in this topic.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.353
GPT teacher head0.482
Teacher spread0.129 · 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 designTheoretical or conceptual
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

Citations6
Published2025
Admission routes1
Has abstractyes

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