From distress to success: Serial winning coaches’ strategies to reestablish adaptive culture and successful performance
Bibliographic record
Abstract
While performance success is idealized in sport, most teams inevitably experience losing streaks or losing seasons, including the most successful coaches. However, how these winning coaches cope with this atypical performance has rarely been studied. Thus, in this study, we explored the post-season processes of seven University team sport coaches with outstanding performance records following an unexpectedly challenging season. The participants averaged 23.3 years of coaching experience and 37 combined National University Championship titles. Following virtual, semi-structured interviews, data was analyzed through a reflexive thematic analysis. Our findings highlighted the centrality of extensive coach reflections during the off-season, including how it contributed to their personal and professional growth. This reflexive period helped coaches develop strategies that were largely culture-focused and included shifting the team mindset. Effective communication was key for facilitating the use of other strategies, such as including athletes in developing their culture and re-setting the culture early in the following season. Indeed, coaches’ reflections and strategies led them to enter the following season feeling more prepared, and all coaches were able to return their teams to their previous standards of excellence within the following two seasons. These findings underscore the importance of coaches’ self-reflection and a proactive process for rebuilding a performative culture when faced with a challenging season. Through introspection, personal and professional development, and strategic cultural adjustments, coaches were able to transform their teams’ performance and dynamics, leading to renewed success and improved outcomes in subsequent seasons.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".