MétaCan
Menu
Back to cohort
Record W2282178047 · doi:10.1177/1747954115624827

Project SCORE! Coaches’ perceptions of an online tool to promote positive youth development in sport

2016· article· en· W2282178047 on OpenAlexaff
Leisha Strachan, Dany J. MacDonald, Jean Côté

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsQueen's UniversityUniversity of Prince Edward IslandUniversity of Manitoba
Fundersnot available
KeywordsPositive Youth DevelopmentPsychologyYouth sportsApplied psychologyTheme (computing)CoachingPerceptionMedical educationAthletesComputer scienceDevelopmental psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Research points to the potential of youth sport as an avenue to support the growth of particular assets and outcomes. A recurring theme in this line of research is the need to train coaches to deliberately deliver themes relating to positive youth development (PYD) consistently in youth sport programs. The purpose of the study was to design and deliver a technology-based PYD program. Project SCORE! ( www.projectscore.ca ) is a series of 10 lessons to help coaches integrate PYD into sport. Four youth sport coaches completed the program in this first phase of this research and were interviewed. The goal of this study was to gain some insights from coaches as they completed the program. Positive comments about the program (i.e. ease of use, success of particular lessons, coach’s personal growth) and challenges regarding teaching positive skills to youth are discussed. These results helped to shape the program and make necessary changes so that it may be used for a larger research study. Other implications and future research directions are discussed.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.044
GPT teacher head0.345
Teacher spread0.301 · 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

Citations61
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sports Science & CoachingSame topicYouth Development and Social SupportFrench-language works237,207