Body Regions Susceptible To Musculoskeletal Injuries In Canadian Armed Forces Pilots
Bibliographic record
Abstract
Musculoskeletal injuries (MSKi) are a significant burden on military pilots. Exploring which body regions are most affected by MSKi and describing injury types is essential for developing preventative strategies. Additionally, gaining insight into between-sex differences may serve as a valuable component of MSKi prevention strategies as the number of female pilots is rapidly increasing. PURPOSE This study is a preliminary investigation of potential sex differences in MSKi rates and regions in actively-serving Canadian Armed Forces (CAF) pilots. METHODS Members of the CAF completed an online cross-sectional survey to capture information about MSKi risks. For the present study, participants who indicated they were actively serving pilots were included. Chi-square analyses were used to evaluate questionnaire items that identified type and region of MSKi to compare response frequency by sex (significance accepted as P < 0.05). RESULTS Seventy-three respondents identified as pilots, of which 53 were male (35.6 + 7.2yrs) and 20 were female (39.9 + 11.1yrs). Figure 1 describes the rates of repetitive strain injuries (RSI) per body region. CONCLUSION RSI rates appear higher for female CAF pilots, and between-sex differences exist in body regions most affected by MSKi. Further research is needed to guide strategies to address sex-disparities and reduce the overall burden of MSKi among CAF pilots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".