A qualitative examination of athletes' perceptions of anxiety using photovoice
Bibliographic record
Abstract
The purpose of this study was to examine the concept of anxiety, what it looks like, what it feels like, and what it means to athletes who are experiencing it. There is a gap that exists within anxiety literature in sport resulting from the vast majority of research examining anxiety that has quantified a subjective experience. Missing from our understanding of anxiety in athletics is a sense of how it is experienced by the athlete. We have an understanding of what factors influence anxiety and how anxiety can be detrimental to sport performance; however, we are missing how it feels. Without such an understanding, we are left with an inadequate analysis of a dynamic and universal phenomenon. Employing the Photovoice method, this study utilized photography and semi-structured interviews to have athletes capture what anxiety is to them and explain their personal experiences. Thematic analysis in conjunction with examination of the participants’ photographs was performed to find common themes among athletes’ experiences with anxiety and capture the essence of these athletes’ stories. Six major themes were identified and they had to do with scheduling/managing athletics and school, expectations for both performance and behaviour, food related worries, managing injuries, body image issues, and small worries that are amplified due to already heightened competition anxiety.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".