A systematic review of drop-out from organized soccer among children and adolescents
Bibliographic record
Abstract
Bronfenbrenner’s bioecological model of human development was used as a framework to systematically review factors associated with drop-out from soccer among children and adolescents. Keyword searches for the population (child or youth or adolescent), sport (soccer or football) and construct of interest (drop-out or attrition or discontinued or quit) identified scholarly peer-reviewed publications from the entire contents of seven databases to 31 December 2013. Publications with participants at any level of organized soccer were eligible for inclusion. The initial search identified 137 studies with 14 ultimately meeting the inclusion criteria, 11 from Europe and 3 from the United States, and 97% (n = 1125,001) of study participants were male. The proportion of children and adolescents who dropped out from one season to the next ranged from 18 to 36%, except for one study of high-level players where the rate of drop-out was 60%. These players felt the time demands of soccer, especially ‘travelling to compete’, were onerous. Prominent person-level factors associated with drop-out were lower perceptions of competence and the lack of fulfilment of basic psychological needs. Contextual factors associated with drop-out were poor relationships with teammates or coaches, lack of enjoyment, lack of opportunity to play, competing time demands, and later birthdate in relation to competitive year. Interactions between the individual and their environment were rarely examined. Future research on drop-out from soccer would benefit from expanding the demographics of those being studied and by concurrently examining interactions between the individual and the environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".