An investigation of the relationship between handedness and health
Bibliographic record
Abstract
Mixed findings surround the notion people with a left hand preference report more autoimmune, mental health, and developmental disorders (e.g., Cazzoli & Chechlacz, 2017; Geschwind & Behan, 1982). How handedness is measured in the literature varies and often does not consider degree of handedness, which is thought to be more representative of neural upper limb lateralization (Luders et al., 2010). Cognitive functioning has shown hemisphere dominance in the brain and performance in these areas are associated with health conditions that are in-line with testosterone theories of handedness. The purpose of this study was to further investigate relationships among handedness, health, and performance on right and bilateral hemisphere dominate tasks. Online assessments completed by 292 participants included; the Waterloo Handedness Questionnaire (Steenhuis et al., 1990), a health questionnaire, a mental rotation task, and the N-back task (Kirchner, 1958). Findings indicated a number of complex interactions. Autoimmune disorders (p=.028), anxiety (p=.024), thyroid disorders (p=.023), speech problems (p=.026), and asthma (p=.017) interacted with handedness on performance on the mental rotation task. Allergies (p=.049), substance abuse (p=.038), and gastrointestinal disorders (p=.016) interacted with handedness on performance on the N-back task. Support was found for measuring handedness as a degree: left and right handers performed better than inconsistent handers. Inconsistent handers who did not report certain health aliments performed better than inconsistent handers who did.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".