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Record W2992480660 · doi:10.18666/tpe-2019-v76-i5-8008

Positive Youth Development and Citizenship Behaviors in Young Athletes: U.S. and Canadian Coaches’ Perspectives

2019· article· en· W2992480660 on OpenAlexaboutno aff
Robert C. Hilliard, Lindsey C. Blom, Mariah Sullivan

Bibliographic record

VenueThe Physical Educator · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesPsychologyYouth sportsCitizenshipPositive Youth DevelopmentCitizenship educationDevelopmental psychologyApplied psychologyPhysical therapyPolitical scienceMedicinePolitics

Abstract

fetched live from OpenAlex

It has been argued that sport is a way for youth to develop psychosocial skills that lead to holistic development. However, participation itself in sport does not lead to this growth; mechanisms for growth must be intentional, often conducted by coaches. Thus, the purpose of this descriptive study was to understand the integration of positive youth development concepts of citizenship into youth sport organizations. One hundred five coaches from the United States and Canada completed an online survey created by the researchers and comprising preexisting measures and newly devised questions. The coaches most heavily emphasized a mastery climate focusing on effort and having fun and emphasized winning the least. Additionally, coaches perceived their youth athletes to learn respect for others, teamwork, and respect for self at the highest rates through participation in their program. Regarding specific techniques for developing citizenship, participants provided many concrete examples in open responses: creating leadership development opportunities for girls in their organization, having league homework programs, and running food drives. Research has identified time as a major barrier to the implementation of opportunities for the explicit transfer of citizenship skills, and the participants provided several methods of growth that are not time intensive. The practical implications and limitations of the results are discussed.Subscribe to TPE

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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 designQualitative
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

Citations8
Published2019
Admission routes1
Has abstractyes

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