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Record W4401976864 · doi:10.3389/fspor.2024.1439822

Delivering project SCORE in competitive youth sport settings

2024· article· en· W4401976864 on OpenAlexafffund
Marta Ferreira, Fernando Santos, María Ángeles Fernández Villarino, Jason Mergler, Leisha Strachan, Dany J. MacDonald

Bibliographic record

VenueFrontiers in Sports and Active Living · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Prince Edward IslandUniversity of Manitoba
FundersFundação para a Ciência e a TecnologiaSocial Sciences and Humanities Research Council of Canada
KeywordsYouth sportsPsychologyAeronauticsApplied psychologyBusinessPhysical therapyAthletesEngineeringMedicine

Abstract

fetched live from OpenAlex

Introduction: This study evaluates the effectiveness of the Project SCORE intervention in fostering Positive Youth Development (PYD) within competitive youth sport settings in Portugal. Project SCORE is an online PYD-focused tool developed to assist coaches in promoting the 4Cs-competence, confidence, connection, and character-within their coaching. Methods: The research involved 13 coaches and 70 youth athletes from football and rowing teams. Methodologically, this study analyzed the pre- and post- Project SCORE intervention data, assessing the perceptions of coaches and athletes towards the development of the 4Cs. Results: Results indicated significant improvements in athletes' perceptions of 4Cs post-intervention, and among coaches' perceptions, there was a significant improvement in the practice and transfer of life skills. Particularly, coaches showed enhanced abilities in fostering life skills and facilitating the transfer of these skills to competitive environments, although some dimensions like sport climate did not sustain positive changes. Discussion: The findings highlight the benefits of customized PYD-based programs in competitive youth sports and suggest the need for further research to enhance their widespread and consistent implementation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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 teacher head, 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

Citations3
Published2024
Admission routes2
Has abstractyes

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