MétaCan
Menu
Back to cohort
Record W3206272033

동화를 활용한 시-지각 훈련 프로그램이 지적장애아동의 작업수행과 시-지각 기능향상에 미치는 효과

2011· article· ko· W3206272033 on OpenAlexaboutno aff
정희승, 박관성

Bibliographic record

Venuenot available
Typearticle
Languageko
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionPsychologyRaw scoreSession (web analytics)Intellectual disabilityVisual perceptionTest (biology)Applied psychologyRaw dataComputer sciencePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

◉ Purpose : The purpose of this study utilizing a fairy tales through the development of a program to perform occupational performance and visual perception with intellectual disabilities at-improved is investigated. ◉ Methods : Visual-perception problems with intellectual disabilities showing 10-year-old man with the consent of parents for children with a total of 15 session, three times a week, 30 minutes, 1 : 1 for utilizing fairy tale - the training was perceived. As for the pre and post program evaluation exercise - Late check-3 (Motor-Free visual perception test-3 ; MVPT-3) was used, performance assessment tasks in order to measure the Canadian Operations (Canadian Occupational Performance Measure : COPM) was used ◉ Results : The children’s fairy tales over the city - through perception training raw score 28 points to 33, ages 6 months to 6 years to 6months to 9 years has improved. ◉ Conclusion : When utilizing fairy tale - the intellectual disabilities with children through training, day to facilitate visual perceptual functions could be seen that effect. In the control group through collective research program is needed to prove a generalization of the will feed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.328
GPT teacher head0.365
Teacher spread0.037 · 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 designNon-randomized trial
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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicDiverse Approaches in Healthcare and Education StudiesFrench-language works237,207