Non-Preferred Limb Performance Following Prolonged Training periods: Performance and Retention of Skills
Bibliographic record
Abstract
The purpose of the current study was to examine if a long-term training program could improve non-preferred limb performance on a battery of standard tasks (Annett Pegboard, Grooved Pegboard Place and Remove Phase) and non-standard tasks (Scroll Task and Line-crossing Task). Upon completion of the training sessions, two new tasks were introduced, Finger Tapping and Fitt’s Law Task, to examine if any performance improvements could be transferred. A second purpose of the study was to learn if a training program could increase the perceived comfort in using the non-preferred hand on the same testing tasks. Participants were assigned to a 1-week training group (3 sessions over 7 days, N = 21), 3-week training group (9 sessions over 21 days, N = 15) or no training control group (N = 20). Training sessions were derived based on the suggestions of the Ackland and Hendrie (2005) study and consisted of 20-30 minute sessions with a focus of non-preferred limb training on multiple tasks. Post-training testing found that though the non-preferred hand never does reach an on par performance with the preferred hand at the same point in time, the Grooved Pegboard Place phase and Scroll tasks, both had significant improvements in non-preferred hand performance over the course of the study. The training was task dependent, as no transfer of training was found on the two transfer tasks. Perceived comfort also improves with repeated exposure to a task, though training and hand improvements were not determining factors, as there were no group differences between tests. The results suggested that a training period of 2- to 3-weeks was all that was required to see improvements in the non-preferred hand, but the learning does appear to be task specific.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".