Coaching at summer camps: What are coaches trying to teach children?
Bibliographic record
Abstract
Summer camps are a popular context in which children engage in sport and provide an opportunity for large numbers of children to learn life skills. However, the extent to which children learn life skills through sport is mainly contingent upon the ways in which coaches deliver programs.The purpose of this study was to examine summer camp coaches' intentions when coaching children. Specifically, we were interested in answering two questions: (a) What do summer camp coaches intend to teach children at summer camps? (b) How do coaches teach these things? Semi-structured interviews were conducted with 24 summer camp coaches. The interviews were transcribed and subjected to inductive content analysis. Data analysis revealed that coaches spent the majority of time teaching technical skills. Coaches perceived they were competent in teaching technical skills and provided numerous examples of how they could teach them based on their own sport experiences and coach education. In contrast, although all the coaches indicated trying to teach at least one life skill to the children, they often struggled to explain how or when they would teach these skills. The majority of coaches perceived life skills would be learnt through technical drills and did not design drills to specifically teach life skills. Thus, we questioned the extent to which life skills were actually being taught during summer camps. These findings suggest that more emphasis could be given to providing coaches with a better understanding of how life skills are developed and more strategies to teach them. Acknowledgments: This study was funded through a grant from the University of Alberta Endowment Fund for the Future Advancement of Scholarship
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".