Examining social support among Olympic athletes and their main support providers
Bibliographic record
Abstract
Olympic athletes face an array of stressors associated with sport (Fletcher & Sarkar, 2012), and social support can be valuable in helping athletes deal with stressors (Gould & Maynard, 2009). However, much of this research has focused on athletes and has not explored the impact that providing support has on support providers. Thus, the purpose of this study was to explore the experience of providing and receiving support between female Olympians and their respective main support providers at the Olympic Games. Five female Olympians and each of their main support providers participated in a semi-structured interview. Data were thematically analyzed (Braun & Clarke, 2006). Participants described the process of support provision (e.g., mode and frequency of contact, emotion regulation), positive and negative outcomes of support provision (including the development of dependence between athletes and supporters), and the impact of organizational structures on the provision and receipt of support. It appeared that the substantial amount of time spent together and the frequent use of technology to provide support fostered the development of athlete dependence on their supporters, with some supporters adopting a parental role with their athletes. Support providers experienced difficulties maintaining other social relationships due to their commitment to supporting their athlete, which led some support providers to perceive a lack of support available for themselves. The results also suggested that the distribution of monetary and non-monetary resources by sport organizations can have a large impact on the provision and receipt of social support.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".