Elite Football Coaches Experiences and Sensemaking about Being Fired: An Interpretative Phenomenological Analysis
Bibliographic record
Abstract
BACKGROUND: Chronic job insecurity seems to be a prominent feature within elite sport, where coaches work under pressure of dismissals if failing to meet performance expectations of stakeholders. The aim of the current study was to get a deeper understanding of elite football coaches' experiences of getting fired and how they made sense of that process. METHOD: A qualitative design using semi-structured interviews was conducted with six elite football coaches who were fired within the same season. Interpretative phenomenological analysis was chosen as framework to analyze the data. RESULTS: The results reflected five emerging themes: Acceptance of having an insecure job, working for an unprofessional organization and management, micro-politics in the organization, unrealistic and changing performance expectation, and emotional responses. CONCLUSION: All coaches expressed awareness and acceptance regarding the risk of being fired. However, they experienced a lack of transparency and clear feedback regarding the causes of dismissal. This led to negative emotional reactions as the coaches experienced being evaluated by poorly defined expectations and by anonymous stakeholders. Sports organizations as employers should strive to be transparent during dismissal. In addition, job insecurity is a permanent stressor for coaches and should be acknowledged and targeted within coach education.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".