The importance of touch in sport: Coaches' and athletes' reflections
Bibliographic record
Abstract
While a growing body of literature exists on the potential for inappropriate touch and maltreatment in sport, research on the importance of touch within this environment is limited. The purpose of this research was to better understand the conditions under which touch is viewed as important in sport. More specifically, this study examined coaches' and athletes' perceptions of the importance of touch, including examples of positive athlete touch, situations in which positive touch is used, and factors affecting athletes' acceptability of touch within this environment. Semi-structured interviews were conducted with 11 coaches and 13 athletes. A variety of sports and sport types were represented. Data were coded using a combination of inductive and deductive coding techniques. All participants shared examples of positive athlete touch in sport including, as some examples: hugs, high fives, pats on the back/head/shoulder, hand shaking, and spotting. A number of reasons for the use of positive touch were identified, which were categorized into the higher order themes of affective, behavioural, cultural, and safety reasons. Factors affecting athletes' general acceptability of touch were also discussed, including demographic, intrapersonal, interpersonal, and contextual variables. Findings are interpreted to suggest that touch is important and necessary within the sport environment; however, in order to be most beneficial, it needs to be facilitated in a way that is best suited to the individual needs of the athlete. Based on the study findings, ways in which touch may be enhanced in sport are suggested and recommendations are posed for future research.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".