Journeying through sport: Athlete narratives of navigating pressure and struggle
Bibliographic record
Abstract
In high-level sport, there is often an overemphasis or fixation on performance which can lead to the decline of important aspects such as athletes' physical health, mental well-being, and quality of life (Smith, 2010; Smith & Sparkes, 2009a). Sport psychology researchers and practitioners have sought to mitigate such threats to well-being by placing an emphasis on helping athletes develop adaptive coping resources and approaches, such as self-compassion (Ferguson et al., 2015; Mosewich et al., 2014; Frentz et al., 2019). To further understand athlete development and support, the purpose of this study was to explore how athletes high in self-compassion story their journey through sport as well as their experiences of navigating pressure and struggle. Three women athletes were recruited based on their high (> 1SD above the mean; Ingstrup et al., 2017) self-compassion scores (i.e., SCS-SF athlete version; Lizmore et al., 2017). Athletes were invited to take part in two loosely structured one-on-one interviews as part of a narrative approach to learn about their sport careers, stories, and experiences (Riessman, 2008). Both narrative thematic and structural analyses are currently in progress in an effort to capture the rich description of athlete experiences as well as the overarching structure of their narrative accounts. This abstract will be updated between Sept. 15 and October 1, 2021 to share key findings and implications.Acknowledgments: This research was supported by the Social Sciences and Humanities Research Council of Canada (SSHRC) Joseph-Armand Bombardier Canada Graduate Scholarship, as well as the University of Alberta's Walter H Johns Graduate Fellowship and Alberta Graduate Excellence Scholarship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 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".