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Record W2792246322 · doi:10.1123/apaq.2017-0061

Psychometric Properties of the Test of Gross Motor Development-3 for Children With Visual Impairments

2018· article· en· W2792246322 on OpenAlexaff
Ali Brian, Sally Taunton, Lauren J. Lieberman, Pamela Beach, John T. Foley, Sara Santarossa

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2018
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsGross motor skillPsychologyInternal consistencyInter-rater reliabilityTest (biology)PsychometricsAudiologyDevelopmental psychologyMotor skillClinical psychologyMedicineRating scale

Abstract

fetched live from OpenAlex

Results of the Test of Gross Motor Development-2 (TGMD-2) consistently show acceptable validity and reliability for children/adolescents who are sighted and those who have visual impairments. Results of the Test of Gross Motor Development-3 (TGMD-3) are often valid and reliable for children who are sighted, but its psychometric properties are unknown for children with visual impairments. Participants ( N = 66; M age = 12.93, SD = 2.40) with visual impairments completed the TGMD-2 and TGMD-3. The TGMD-3 results from this sample revealed high internal consistency (ω = .89–.95), strong interrater reliability (ICC = .91–.92), convergence with the TGMD-2 ( r = .96), and good model fit, χ 2 (63) = 80.10, p = .072, χ 2 / df ratio = 1.27, RMSEA = .06, CFI = .97. Researchers and practitioners can use the TGMD-3 to assess the motor skill performance for children/adolescents with visual impairments and most likely produce results that are valid and reliable.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.273
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations45
Published2018
Admission routes1
Has abstractyes

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