Nurturing holistic talent, addressing systemic inequity: Canadian coaches’ insights on optimizing youth soccer talent identification and development
Bibliographic record
Abstract
Evidence suggests that youth talent identification and development systems (TIDS) are lacking in quality due to various complex and multifaceted factors specific to youth players. Given the importance of consistently identifying and developing high-achieving players, soccer organizations and coaches rely heavily on effective and productive youth TIDS. This study explored the perspectives of youth coaches in Canada to: (1) enhance the capacity of youth soccer organizations and coaches to make informed decisions about TIDS, (2) maximize the advancement of youth players, (3) identify approaches, practices, and priorities for developing youth players to compete at higher levels. Using a qualitative research approach, semi-structured interviews were conducted with 16 National Licensed coaches from professional youth academy or Canada's Youth National Teams. A thematic analysis identified three central themes: (1) Views on TIDS are specific to organizations and coaches, (2) More inclusive and sustainable TIDS are necessary for success, and (3) Exposure to holistic talent development environments is essential. The findings emphasized informed decisions to establish coherent, effective, and player-centered systems through professionalized organizations and coaches, a collective philosophy with clear standards, and prioritization of players’ needs. Inclusive environments increase access to opportunities and resources that motivate players with diverse backgrounds and talents, supporting longterm engagement. An equity-based lens expands the player pool through equitable and sustainable opportunities for all players. A holistic approach to promoting psychosocial well-being and personal development is necessary for meaningful experiences within TIDS. Flexible, differentiated, and empowering approaches are needed for playercentered pathways that meet players’ evolving needs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".