Coach transitions: Influence of interpersonal and work environment factors.
Bibliographic record
Abstract
Each year many coaches leave their positions (e.g., experience transitions).This change can 2 have damaging effects on athletes, sports programs, and coaches (O'Connor & Bennie, 2006; 3 Raedeke, Warren, & Granzyk, 2002).Consequently, understanding the factors that influence 4 coach transitions is pertinent.To address this need, two qualitative descriptive studies were 5 conducted to examine the work-environment factors that influence coach transitions.In study 6 one, 21 full-time, part-time, and volunteer coaches from across Canada participated in semi-7 structured interviews.Through a process of inductive content analysis (Miles & Huberman, 8 1994), 10 lower-order themes describing reasons coaches transitioned between positions were 9 identified.These 10 lower-order themes were grouped into four higher-order themes: 1) 10 interpersonal considerations, 2) work demands, 3) career concerns, and 4) positive coaching 11 experiences.Building on study one, study two sought to explicitly explore the positive and 12 negative factors influencing transitions with a further 14 coaches.Following analysis, two 13 overarching themes depicting reasons for transitions were identified: Seeking opportunities to 14 be more successful or achieve more success, and leaving a negative or challenging work 15 environment.These two overarching themes were underpinned by a further six higher-order 16 themes.Overall, results indicated that there are various factors influencing coaches' 17 transitions, and that such transitions can be motivated by positive factors (i.e., opportunities 18 for career advancement), or negative factors (i.e., leaving an undesirable work environment).19Findings highlight the importance of practitioners and sports organizations providing support 20 to enable coaches to advance their career and also provide better support and strategies to 21 optimize coaches' working environment.22 23
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".