Physical Activity and Injuries Relating to Physical Fitness of Professional Firefighters
Bibliographic record
Abstract
Abstract Introduction The profession of a firefighter involves multiple factors that directly or indirectly impact on the person’s health. The aim of this study was to establish the correlation between physical fitness of the selected group of firefighters with respect to anthropometric parameters, additional physical activity, and injury rate. Material and methods The study examined 77 men who worked for the State Fire Service (age: 28.87±9.84 years, body mass: 82.13±9.37 kg, body height: 180.12±6.39 cm). Of he study group, 53% of the fire-fighters had normal BMI, 42% were overweight and 5% had first degree obesity. The study used a survey questionnaire concerning the anthropometric data, previous injuries, physiotherapeutic procedures following the injury, and participants’ involvement in additional physical activity. A physical fitness test battery was used to determine the correlation between physical fitness and: age, BMI and additional physical activity and injury rate. Results Musculoskeletal injuries accounted for 51% of all injuries. Of all injuries, 56% were occupational. The correlations between the results of the handgrip test were insignificant with respect to BMI and age, whereas in other tests, the correlations were statistically significant (p<0.05). No differences were observed in the results of handgrip test between the group of firefighters who were and those who were not involve in additional physical activity. Comparison of the results obtained by firefighters following the injury and without previous injuries revealed a statistically significant difference (p<0.05) in the 4 x 10 m shuttle run. Conclusions Age and higher values of BMI are the factors that reduce the level of physical fitness of firefighters. Involvement in additional physical activity is a factor in improving physical fitness of firefighters. Previous injuries the firefighters had suffered did not have an effect on their physical fitness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".