Self-determined motivation, social identification and the mental health of adolescent male team sport participants
Bibliographic record
Abstract
This study assessed whether participants’ social identification with their team moderated the association between self-determined motivation and mental health and wellbeing among adolescent male team sports participants. Participants were 383 adolescent male team sports participants. Moderated regression analyses showed that, at average and higher levels of social identification, self-determined motivation was negatively associated with psychological distress. At higher levels of social identification, self-determined motivation was positively associated with wellbeing. At lower levels of social identification, there was no relationship between self-determined motivation and psychological distress or wellbeing. The mental health benefits associated with participation in organized sports may systematically vary according to levels of social identification, with the association magnified among those with higher levels of social identification. Attention to social identification processes in youth sport may be beneficial, and this should be tested using experimental designs. Lay Summary: Self-determined forms of motivation are associated with better mental health outcomes among adolescent male sport participants. These outcomes may be magnified when sport participants strongly identify with their sports teams. Practical implicationsSelf-determined forms of motivation may be the first focus for coaches and sport psychology practitioners.Where self-determination is high, enhancing social identification among sport participants could provide extra benefits for mental health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".