The Influence of Hand Immersion Duration on Manual Performance
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of hand immersion duration on manipulative ability and tactile sensitivity. BACKGROUND: Individuals in maritime settings often work with hands that have been immersed in water. Although research has shown that hand immersion duration differentially impacts skin adhesion and tactile sensitivity, the effect of hand immersion on manipulative ability has not been directly tested. Given how critical manipulative ability is for the safety and performance of those working at sea, the effect of hand immersion duration on manual performance was investigated. METHOD: Tests of manipulative ability (Purdue Pegboard, Grooved Pegboard, reef knot untying) and tactile sensitivity (Touch-Test) were completed following no-exposure, short-exposure, and long-exposure hand immersions in thermoneutral water. RESULTS: Compared to the no immersion condition, the Purdue Pegboard performance was reduced in both immersion conditions (short exposure, -11%; long exposure, -8%). A performance decrement was only observed in the short exposure condition (+15% in time to complete task) for the reef knot untying task. There were no statistical differences in the Grooved Pegboard or Touch-Test scores between exposure conditions. CONCLUSION: Immersing the hands in water decreases manipulative ability except for when object properties reduce the slipperiness between the hand and object. APPLICATION: Manual performance in a wet environment may be conserved by designing tools and objects with edges and textures that can offset the slipperiness of wet hands. To maintain safety, the time requirements for working with wet hands needs to be considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".