Analyzing continuous coach education courses in Portugal: Implications for youth development
Bibliographic record
Abstract
Continuous coach education courses (CCEC) are considered a crucial vehicle for exposing coaches to topics (e.g., nutrition, mental health, positive youth development) not addressed in their initial coach training. CCEC can help coaches develop well-rounded coaching practices based on youth's physical, social, emotional, and psychological needs. The purpose of the study was to analyze the distribution of CCEC offered in Portugal between 2014 and 2020. Descriptive and inferential statistics were used to analyze (a) the number of CCEC offered; (b) hours devoted to each topic; (c) the types of organizations who delivered CCEC; (e) the format of CCEC (i.e., online or in-person); (f) the geographical distribution of CCEC throughout Portugal; (g) the number of coach participants per topic. The results indicate that courses addressing mental health, social justice, positive youth development, and sleep hygiene are seldomly delivered in Portugal, meaning that coaches have few opportunities to be exposed to and learn about these important topics. Thus, our results suggest CCEC in Portugal may not be extending coaches’ knowledge much beyond topics covered in their initial coach training. The lack of breadth in training may help perpetuate the emphasis on the technical, tactical, and physical development of youth with the Portuguese youth sport system. Based on the results, implications for youth development 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".