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Record W2554880716 · doi:10.1371/journal.pone.0166337

Using Bishop’s Card Reaching Task to Assess Hand Preference in 8- to 10-Year-Old Czech Children

2016· article· en· W2554880716 on OpenAlexaff
Martin Musálek, Sara M. Scharoun Benson, Pamela J. Bryden

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsWilfrid Laurier UniversityNipissing University
FundersGrantová Agentura České Republiky
KeywordsHand preferencePreferenceTask (project management)PsychologyCzechDiceCognitive psychologyTest (biology)LateralityDevelopmental psychologyStatisticsMathematicsBiology

Abstract

fetched live from OpenAlex

Hand preference is one of the most apparent functional asymmetry in humans. Under contralateral control, performance is more proficient with the preferred hand; however, the difference between the two hands is greater in right handers, considering left handers generally display less cerebral lateralization. One method of evaluating hand preference is Bishop's card reaching task; however, information regarding validity and sensitivity with children in limited. This study assessed the relationship between Bishop's card reaching task and five hand preference tasks in 8- to 10-year-old typically-developing children from the Czech Republic (N = 376). Structural equation modelling identified a one factor model as the most suitable, including Bishop's card reaching task and three hand preference tasks (ringing, throwing, and rolling with dice). The factor validity (.89) and sensitivity of Bishop's card reaching task (90% to 97%) provided a very good identification of hand preference. These results support the suitability of Bishop's card reaching task as a separate test for determining hand preference in children. Accordingly, we suggest that the assessment of handedness, particularly in neurodevelopmental disorders where the proportion of right-handers and left-handers is disrupted (e.g., children with DCD or ADHD), should make use of Bishop's card reaching task alongside other unimanual tasks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.235
GPT teacher head0.309
Teacher spread0.074 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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