Gender differences in load carriage injuries of Australian army soldiers
Bibliographic record
Abstract
BACKGROUND: With the removal of gender restrictions and the changing nature of warfare potentially increasing female soldier exposure to heavy military load carriage, the aim of this research was to determine relative risks and patterns of load carriage related injuries in female compared to male soldiers. METHODS: The Australian Defence Force Occupational Health, Safety and Compensation Analysis and Reporting workplace injury database was searched to identify all reported load carriage injuries. Using key search terms, the narrative description fields were used as the search medium to identify records of interest. Population estimates of the female: male incident rate ratio (IRR) were calculated with ninety-five percent confidence interval (95% CI) around the population estimate of each IRR determined. RESULTS: Female soldiers sustained 10% (n = 40) of the 401 reported injuries, with a female to male IRR of 1.02 (95% CI 0.74 to 1.41). The most common site of injury for both genders was the back (F: n = 11, 27%; M: n = 80, 22%), followed by the foot in female soldiers (n = 8, 20%) and the ankle (n = 60, 17%) in male soldiers. Fifteen percent (n = 6) of injuries in female soldiers and 6% (n = 23) of injuries in males were classified as Serious Personal Injuries (SPI) with the lower back the leading site for both genders (F: n = 3, 43%: M: n = 8, 29%). The injury risk ratio of SPI for female compared to male soldiers was 2.40 (95% CI 0.98 to 5.88). CONCLUSIONS: While both genders similarly have the lower back as the leading site of injury while carrying load, female soldiers have more injuries to the foot as the second leading site of injury, as opposed to ankle injuries in males. The typically smaller statures of female soldiers may have predisposed them to their observed higher risk of suffering SPI while carrying loads.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".