Impact of Job Insecurity on Psychological Well- and Ill-Being among High Performance Coaches
Bibliographic record
Abstract
BACKGROUND: The evaluative nature of high performance (HP) sport fosters performance expectations that can be associated with harsh scrutiny, criticism, and job insecurity. In this context, (HP) sport is described as a highly competitive, complex, and turbulent work environment. The aim of this longitudinal, quantitative study was to explore whether HP coaches' perceptions of job insecurity and job value incongruence in relation to work would predict their psychological well- and ill-being over time. METHODS: = 299) responded to an electronic questionnaire at the start, middle, and end of a competitive season, designed to measure the following: job insecurity, values, psychological well-being (vitality and satisfaction with work), and psychological ill-being (exhaustion and cynicism). Structural equation model analyses were conducted using Mplus. RESULTS: Experiencing higher levels of job insecurity during the middle of the season significantly predicted an increase in coaches' psychological ill-being, and a decrease in their psychological well-being at the end of the season. However, value incongruence did not have a significant longitudinal impact. CONCLUSIONS: These findings cumulatively indicate that coaches' perceptions of job insecurity matter to their psychological health at work. Consequently, it is recommended that coaches and organizations acknowledge and discuss how to handle job security within the HP sport context.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".