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Record W4220827907 · doi:10.1177/21582440221087272

The Benefits and Barriers of Sport for Children From Low-Income Settings: An Integrative Literature Review

2022· article· en· W4220827907 on OpenAlexaff
Heather Nelson, Shelley Spurr, Jill Bally

Bibliographic record

VenueSAGE Open · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCoachingThematic analysisPsychologyMental healthSocial benefitsApplied psychologyQualitative researchSociology

Abstract

fetched live from OpenAlex

The purpose of this integrative review was to examine the existing literature about the emotional and social benefits, as well as barriers and facilitators to sport participation for children from low-income settings. Thematic analysis was performed yielding three major themes: (1) emotional benefits of sport participation; (2) social benefits of sport participation; and (3) barriers and facilitators to sport participation. Overall, the thirteen studies showed positive emotional and social benefits for sport participation; however, one study found decreased mental health and one reported no significant findings. Facilitators such as psychological safety and social support are needed to encourage sport participation as significant barriers to sport participation continue to exist for children from low-income settings. Future areas of research include more longitudinal studies related to the role of sport, the environment, coaching style, and investigation into why participation rates continue to be decreased for children from low-income settings despite added social supports.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
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 head0.010
GPT teacher head0.296
Teacher spread0.287 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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