International consensus on the definition of functional training: Modified e-Delphi method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.165 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".