Development of a Core-Stability Model: A Delphi Approach
Bibliographic record
Abstract
CONTEXT: Despite widespread acceptance, there is currently no consensus on the definition, components, and the specific techniques most appropriate to measure and quantify core stability. OBJECTIVE: To develop a comprehensive core-stability model addressing its definition, components, and assessment techniques. DESIGN: Delphi technique. SETTING: University laboratory. PARTICIPANTS: 15 content experts from United States and Canada, representing a variety of disciplines. MAIN OUTCOME MEASURE: The authors distributed an open-ended questionnaire pertaining to a core-stability definition, components, and assessment techniques specific to each expert. They collected data over 2 rounds of telephone interviews. They concluded data collection once a consensus was achieved that equated with 51% agreement among respondents. RESULTS: The authors developed a working definition of core stability as the ability to achieve and sustain control of the trunk region at rest and during precise movement. Eighty-three percent of the experts considered the definition satisfactory. Therefore, the definition was accepted. Furthermore, the experts agreed that muscles (14/15 = 93.3%) and neuromuscular control (8/12 = 66.7%) were components of core stability. Assessment techniques were identified and inconsistencies were highlighted; however, no consensus was established. CONCLUSIONS: A consensus core-stability definition was created and 2 components were identified. However, of the initial definitions provided by the experts, no 2 were identical, which revealed the inconsistencies among experts and the importance of this study. Nonetheless, the goal of obtaining a consensus definition was obtained. Although a consensus for the assessment techniques of core stability could not be reached, it was a beneficial starting point to identify the inconsistencies that were discovered among the content experts.
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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 | 0.125 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".