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Record W2511987540 · doi:10.5539/ijps.v8n3p215

Response Patterns of Art Based Intervention for Self-Regulation among Athletes

2016· article· en· W2511987540 on OpenAlexvenueno aff
Jolly Roy, Garry Kuan, Tneh Khai Chy

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)AthletesIntervention (counseling)Competition (biology)Self-controlSocial psychologyApplied psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Competition outcome often shows a player’s ability to self-regulate and be in control. This study examined the response patterns of Clay Button Method (CBM), an adapted art approach, in a sport context, among Paralympics, footballers and injured athletes for self-regulation. The article enlists the steps involved in CBM and the percentage of change is noted across six sessions among the participants. CBM focuses on teaching self-regulation by identifying individual areas of strengths and areas of concern, to build personal resources and weed out possible deficits. The participants engaged in CBM using playing clay, making their own self-image and exploring any unresolved emotional issues and bringing in desired changes. The response patterns revealed a trend where the players were able to self-regulate by reducing unproductive thoughts and strengthening personal resources. The study provides preliminary findings for introducing innovative approach in a sport context.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0030.000

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 head0.068
GPT teacher head0.405
Teacher spread0.337 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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