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Record W2740949706 · doi:10.22054/jpe.2017.20693.1526

اثربخشی بازی با کلمات احساسی بر کاهش نارسایی هیجانی دانش آموزان با ناتوانی یادگیری

2017· article· fa· W2740949706 on OpenAlexaboutno aff
اکرم آبباریکی, بیتا شلانی

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languagefa
FieldPsychology
TopicChild Therapy and Development
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Specific learning disability is a neurodevelopmental disorder. Students with this disability, in addition to academic problems, have high levels of socio-emotional problems. The aim of this study was the effectiveness of play with feeling words on reducing alexithymia in students with learning disabilities. This study is quasi-experimental with pre-post test. The population included all students second to six grade with learning disabilities in Kermanshah that 24 student selected and randomly assigned in two experimental and control groups. The research instrument was Toronto Alexithymia Scale. The experimental group plays feeling words eight sessions 30-minute and twice a week. Covariance analysis indicated that play therapy with feeling words caused reduction in children's alexithymia (P<0/001). Results of this study showed that play therapy with feeling words can be used as a convenient way to help students with learning disorders to reduce their alexithymia. This type of play therapy with symbolic expression of feelings increases ability to express of negative emotions such as anxiety and led to the adaptation.

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)0.002
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 score0.080
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0800.026

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 head0.407
GPT teacher head0.634
Teacher spread0.227 · 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
Published2017
Admission routes1
Has abstractyes

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