Exploring the utility of analogies in motor learning after stroke
Bibliographic record
Abstract
Individuals who have experienced a stroke need to (re)learn motor skills. Analogy learning has been shown to facilitate motor learning in sports and may also be an attractive alternative to traditional approaches in therapy. The aim of this study was to assess the feasibility and utility of analogies to improve the walking performance in long-term stroke survivors. Three men aged 76, 87 and 70 years who were 6, 1 and 3 years poststroke, respectively, presented with different walking deficits. An analogy, targeted at improving the walking performance was designed with the help of each participant. During a 3-week intervention period, the analogy was practiced once weekly under supervision and daily at home. To assess feasibility, a structured interview was conducted at the end of the intervention period. To assess utility, walking performance was assessed using the 10-Metre Walking Test. All three participants were supportive of the feasibility and benefits of analogy learning. Two of the participants had a meaningful improvement on the 10-Metre Walking Test (0.1 and 0.3 m/s). The third participant did not improve most likely because of medication issues during the week of the retest. Developing analogies in therapy is a creative and challenging process, as analogies must not only guide the correct movement pattern, but also be meaningful to the individual. However, as participants were supportive of the use of analogies, and positive trends were seen in walking speed it seems worthwhile to pursue the use of analogies in 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".