Terminology, Taxonomy, and Facilitation of Motor Learning in Clinical Practice: Protocol of a Delphi Study
Bibliographic record
Abstract
BACKGROUND: Facilitating motor learning in patients during clinical practice is complex, especially in people with cognitive impairments. General principles of motor learning are available for therapists to use in their practice. However, the translation of evidence from the different fields of motor learning for use in clinical practice is problematic due to lack of uniformity in definition and taxonomy of terms related to motor learning. OBJECTIVE: The objective of this paper was to describe the design of a Delphi technique to reach consensus on definitions, descriptions, and taxonomy used within motor learning and to explore experts' opinions and experiences on the application of motor learning in practice. METHODS: A heterogeneous sample of at least 30 international experts on motor learning will be recruited. Their opinions regarding several central topics on motor learning using a Delphi technique will be collected in 3 sequential rounds. The questionnaires in the 3 rounds will be developed based on the literature and answers of experts from earlier rounds. Consensus will be reached when at least 70% of the experts agree on a certain topic. Free text comments and answers from open questions on opinions and experiences will be described and clustered into themes. RESULTS: This study is currently ongoing. It is financially supported by Stichting Alliantie Innovatie (Innovation Alliance Foundation), RAAK-international (Registration number: 2011-3-33int). CONCLUSIONS: The results of this study will enable us to summarize and categorize expert knowledge and experiences in a format that should be more accessible for therapists to use in support of their clinical practice. Unresolved aspects will direct future research.
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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 | 0.117 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".