Developing a revised definition of the Bobath concept: Phase three
Bibliographic record
Abstract
OBJECTIVE: To develop a revised definition of the Bobath concept that incorporates the perspectives of members of the International Bobath Instructors Training Association (IBITA). METHODS: A three-phase consensus building design utilizing (i) focus groups; (ii) survey methods; and, (iii) real-time Delphi. This paper presents the findings from the real-time Delphi, an iterative process to collect and synthesize expert opinions anonymously, provide controlled feedback, with the overall goal of achieving consensus. RESULTS: One hundred and twenty-one IBITA members participated in the real-time Delphi. Over three Delphi Rounds, consensus was reached on six overarching conceptual statements and 11 statements representing unique aspects of Bobath clinical practice. One statement that aimed to describe the Bobath clinical term of "placing" was eliminated in Round One due to participant reservations that a text description was insufficient for this term. Seven statements underwent minor wording revisions in Round Two and Three to improve sentence clarity. CONCLUSION: Using the real-time Delphi, we were successful in gaining consensus in an expert group on a series of statements on which a revised definition of the Bobath concept could be based.
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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 | 0.103 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".