Unmasking the divide: Musculoskeletal injury and physical fitness disparities among military and emergency responders
Bibliographic record
Abstract
LAY SUMMARY Injuries have a considerable impact on military members, first responders, and health care providers. Research often examines these populations together, but no direct comparison of physical fitness, injuries, and reproductive health within these groups has been done. The authors examined physical fitness relationships among injury history, occupation (i.e., military and non-military [NM] arduous occupations; i.e., police, firefighting, paramedic, health care), and whether an individual had carried a pregnancy to 20 weeks. The study found that the military group had more back, hip, foot, and lumbopelvic hip complex injuries, but the NM group had more thumb injuries. NM participants did better on several physical fitness tests, and those with a history of acute injury had better lower body strength than military participants with acute injury. NM participants with a history of acute injury who had never given birth had better relative lower body strength than those who had given birth; the opposite was seen in the military group. These findings suggest that initiatives and research supporting females employed in arduous occupations should consider differentiating between military and NM. Moreover, injury and a history of childbirth are important historical complexities that should be included when examining physical fitness outcomes in these populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".