Mental preparation strategies and firefighter’s performance under stress
Bibliographic record
Abstract
Firefighters have to perform in high demanding environments, which require the ability to cope with stress. Research has shown that performance as well as stress reduction is affected by mental preparation strategies such visual imagery, activation, self-confidence, attentional control, relaxation, goal setting and self-talk. The outcome of mental preparation strategies is a psychological state called 'mental readiness'. Mental readiness emerged from sport psychology and was found to be important for performance improvements of top athletes. However, until now mental readiness has never been used to predict firefighter performance. Thus, the purpose of this study is to examine the relationship between mental readiness dimensions and perceived stress, success and task performance of firefighters. A sample of 68 firefighters was surveyed. The mental readiness dimensions as well as performance and stress issues were measured with the Mental Readiness Scale (MRS). Correlations and multiple regression analyses showed mixed results. Some scales like self-talk and goal setting did not significantly correlate with performance, success or stress of firefighters, whereas other sub scales such as visual imagery, activation and attentional control showed medium to large effects. The results reveal that firefighters benefit differently from the specific mental preparation strategies. Some strategies help firefighters to cope with stress. Other strategies seem to improve task performance and success. The results can be used to design evidence-based mental training programs to improve firefighter's performance and stress reduction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".