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Record W4388488853 · doi:10.1123/tsp.2022-0088

Missing Out, as Well: The Absence of Youth Sports and Its Effect on Parents During the COVID-19 Global Pandemic

2023· article· en· W4388488853 on OpenAlexaff
Niël Strydom, Alex Murata, Jean Côté

Bibliographic record

VenueThe Sport Psychologist · 2023
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsQueen's University
Fundersnot available
KeywordsThematic analysisPsychologyDisengagement theoryFeelingPerceptionYouth sportsContext (archaeology)Coronavirus disease 2019 (COVID-19)PandemicPositive Youth DevelopmentSocial psychologyDevelopmental psychologyQualitative researchAthletesSociologyMedicineGerontology

Abstract

fetched live from OpenAlex

In December of 2019, COVID-19 began spreading globally. As a result, many youth sport organizations were forced to halt programming. While unfortunate, this imposed disengagement from youth sport provided an opportunity to explore what youth sport means to parents, being that this was the first time many were without it. As such, researchers aimed to explore the attitudes and perceptions of youth sport parents regarding their child’s sport participation in its absence. Semistructured interviews were conducted to explore these perceptions, and three themes were constructed through thematic analysis. Findings suggest that sport parents miss their experiences as “live-in” sports fans of their child’s sport participation due to the absence of their spectator experiences, social opportunities, and feelings of success, which drive their motivation for continued involvement. Understanding parental motivations to support youth sport participation may lead future researchers to uncovering the influences of parental behavior in the youth 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)0.004
metaresearch head score (Gemma)0.015
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
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 head0.062
GPT teacher head0.376
Teacher spread0.314 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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