Role of Coaches, Mentors, and Facilitators
Bibliographic record
Abstract
The coach– athlete relationship is at the heart of coaching (Jowett, 2017) and is central to determining the effects of youth sport participation. Coaches and other programme facilitators have unique opportunities to influence the youth they are working with (Rutten et al, 2007; Côté, Strachan, and Fraser-Thomas, 2008). There may not be a consensus on exactly what differentiates effective from ineffective coaches, but factors such as leadership, expertise, motivation, education, and experience are often cited, together with an ability to form meaningful relationships with youth (Côté and Gilbert, 2009). Some suggest that coaches must deliberately and systematically integrate life skills development and transfer strategies within their coaching to optimize athlete development (Camiré et al, 2011; Weiss, Bolter, and Kipp, 2014; Bean et al, 2018). The teaching of life skills in sport involves coaches deliberately teaching skills such as goal setting, communication, managing emotions, and developing effective relationships (Camiré and Trudel, 2010). Additionally, coaches involved in programmes for at-risk youth should be sensitive and responsive towards their developmental needs and be reflective of their own behaviour as a role model to maximize the potential of sportbased interventions (Spruit et al, 2018b). Coaches usually understand the value of life skills integration within sport-based programmes, but they do not always have the knowledge and competencies to explicitly integrate life skills into their coaching practices (Bean and Forneris, 2017). Most youth sport coaches are volunteers who receive little training about youth development through sports (Newman et al, 2018). Training tends to focus on practice design and skill development, rather than on the promotion of positive developmental outcomes. High school sport coaches are typically not equipped with the knowledge, tools, and skills required to deliberately teach life skills (Camiré et al, 2020). Most coaches have little training on how to structure suitable environments to facilitate youth development (Camiré et al, 2011). Although coaches and volunteer staff can coherently describe their coaching practices, they are not particularly interested in theoretical explanations of these practices (Cushion and Partington, 2016). In British Columbia, for example, coaches involved in community-based sports programmes receive very little training. The training they receive, if any, tends to focus on the technical aspects of coaching, the rules of the game, issues of physical safety and potential liabilities, and sometimes awareness raising around issues such as sexual harassment or other inappropriate behaviour.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".