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Record W2955774139

A review of trends in youth sport psychology research

2015· review· en· W2955774139 on OpenAlexaff
Shannon R. Pynn, Kacey C. Neely, Nicholas L. Holt

Bibliographic record

Venuenot available
Typereview
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSport psychologyPsychologyApplied psychologySocial scienceSocial psychologySociology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to review trends in youth sport psychology research. Four major sport psychology journals (The Sport Psychologist, the Journal of Sport and Exercise Psychology, the Journal of Applied Sport Psychology and Psychology of Sport and Exercise) were reviewed to identify studies of youth sport. Articles were included based on the age of the sport participants (age range= 5-18 years) and their involvement in organized youth sport. The title, abstract, and contents of each article were analyzed to derive the main themes, which were then organized by journal and decade of publication. A total of 3079 articles containing original data were published in the journals over a span of 35 years with 470 articles meeting the inclusion criteria. Articles on motivation (n=91), social influences (n=84), anxiety/stress (n=47) and self-perceptions (n=43) appeared most frequently across all four journals. We assumed the number of articles published on a particular topic reflected interest in that topic within the discipline of sport psychology. Hence, a decrease in the number of articles about motivation and an increase in the number of social influence articles published in the last decade would indicate that motivation is a topic that has been exhausted by youth sport researchers while research on social influences is an emerging trend. The findings generate a discussion on why such trends occur and what impact they have on future research.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0180.018
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.435
GPT teacher head0.565
Teacher spread0.130 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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