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Record W4312226006 · doi:10.1123/apaq.2022-0049

Results From South Korean 2022 Para Report Card on Physical Activity for Children and Adolescents With Disabilities

2022· article· en· W4312226006 on OpenAlexaff
Jeongmin Lee, Ki-Taek Oh, Jihee Min, Seon Young Goo, Eun‐Young Lee, Kyoung June Yi, Jinmoo Heo, Joon Sung Lee, Dong‐Il Kim, Wonsang Shin, Kwon-il Kim, Yeon Soo Kim, Justin Y. Jeon

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of ManitobaQueen's University
FundersYonsei University
KeywordsReport cardRubricGrading (engineering)PsychologyStrengths and weaknessesGovernment (linguistics)Physical activityMedical educationApplied psychologyMedicinePhysical therapyMathematics educationPedagogySocial psychologyEngineering

Abstract

fetched live from OpenAlex

South Korea has developed its first Para Report Card on physical activity (PA) for children and adolescents with disabilities. Five national surveillance databases were used to evaluate PA indicators based on the benchmarks and grading rubric provided by Active Healthy Kids Global Alliance. Report card evaluation committees were invited to grade and assess the results using strengths, weaknesses, opportunities, and threats analysis. Five indicators (overall PA, D+; organized sports and PA, D-; active transportation, D-; physical fitness, D+; and government, A+) and one additional indicator (sleep, C-) were assigned a letter grade. The other five indicators were graded as incomplete. The Para Report Card revealed a significant gap between the behavioral-indicator grades (D- to D+) and the policy-indicator grade (A+), suggesting that government strategies and investment have not yet been translated into behavioral PA among children and adolescents with disabilities.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.005

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 head0.020
GPT teacher head0.270
Teacher spread0.249 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueAdapted Physical Activity QuarterlySame topicObesity, Physical Activity, DietFrench-language works237,207