Sample or specialize? Exploring youth sport coaches' perspectives and practices regarding sport specialization and sport sampling
Bibliographic record
Abstract
Previous research associates early sport specialization with negative athlete outcomes such as injury and burnout. Despite the growing body of research cautioning against specializing early, many young athletes continue to pursue one sport at the exclusion of others, and little is known about coaches’ roles in influencing young athletes’ decisions to specialize or undertake multisport experiences. This research examines the perceptions and practices of a sample of youth sport coaches regarding sport sampling and sport specialization, and it investigates how a sample of coaches perceive and implement the recommendations contained in Sport for Life Canada’s (2019) Long-Term Development in Sport and Physical Activity 3.0 framework (LTD model). Specifically, nine youth club basketball coaches from Manitoba who are working with athletes in the Train-to-Train age category (females ages 11-15, or males ages 12-16) completed a questionnaire and participated in a one-on-one semistructured interview to gather in-depth information about each coach’s perceptions and behaviours regarding sport sampling, sport specialization, and long-term athlete development, as well as whether their philosophical perceptions and coaching behaviours align with the LTD model. From the questionnaires and interviews it was found that: (1) coaches are committed to the principle that athletes should sample but have difficulty explaining how their beliefs translate into action with their teams, (2) athletes continue to undertake too much training, (3) the youth sport system is broken, (4) coaches are aware of the LTD model but lack the tools to apply it with their teams, and (5) coaches apply a variety of strategies to accommodate athletes who play multiple sports while maintaining expectations of commitment and hard work within their programs. As coaches participating in this study believe that athletes should sample and provide flexibility in their programming to accommodate athletes who play multiple sports, yet many athletes continue to specialize and overtrain, systemic factors remain in the youth sport system that prevent coaches from effectively implementing the LTD model. These results have potential applications in coach education as well as for sport organizers and governing bodies that make programming decisions that impact athlete development.
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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 | 0.006 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".