Promoting Positive Mental Health in Portuguese and Brazilian Youth Sport: The Roles and Responsibilities of Policymakers, Coaches, and Coach Developers
Bibliographic record
Abstract
Mental health is positioned as a key outcome of organised youth sport participation and is a public health priority around the world. However, discussions on the priority of and intersection between mental health and youth sport are just beginning in some countries. Portugal and Brazil have made efforts to reflect on the current state of sport and have considered initiatives targeting mental health promotion. Therefore, the purpose of the current paper is to advocate for policymakers, coaches, and coach developers to deploy efforts to promote and protect the mental health of youth athletes and expand on possible ways to achieve this. Portugal and Brazil are used as cases to discuss the processes and strategies needed for promoting mental health. These countries were chosen because they share many sociocultural similarities and have few provisions in place for promoting mental health in youth sport. This manuscript is intended to serve as an instigator for creating awareness among decision makers (e.g., policymakers and coach developers), in both countries and across their sport systems, about the relevance of promoting mental health in youth sport. Potential challenges to promoting and protecting mental health are discussed, and practical implications for coaching and coach education are offered.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".