The Relationship Between Parity Status and Musculoskeletal Injury Prevalence in Female Members of the Canadian Armed Forces
Bibliographic record
Abstract
Musculoskeletal injuries (MSKi) place a significant burden on females in arduous occupations, like military service members, first responders (public safety personnel), and healthcare workers. In the Canadian Armed Forces (CAF), females are more likely to release due to an MSKi than their male peers, and male healthcare workers demonstrate a lower relative risk for all injuries. Female reproductive health (e.g., pregnancy) may contribute to the sex-disparities in MSKi prevalence, but the relationship between parity status and MSKi has not been explored. The multistage approach applied to examine the relationship between parity status and MSKi in CAF members was as follows: (i) Self-reported MSKi and reproductive health data were collected via an online questionnaire and analyzed using chi-square and logistic regression tests. (ii) Physical testing to assess flexibility (sit-and-reach), muscular power (long jump and medicine ball throw), movement competency (bodyweight overhead squat test), muscular strength (4 repetition maximum back squat and bench press), muscular endurance (Biering-Sorenson test, single-leg wall sit, and push-ups), aerobic capacity (graded treadmill VO_2max test). MSKi history and reproductive health data were collected via a pre-screening questionnaire. Physical fitness was compared by parity status and MSKi history using one and two-way analysis of variance (ANOVA) tests. (iii) A study similar to step ii was conducted in females employed as first responders or healthcare providers. (iv) A 3-way ANOVA was performed to compare the CAF and non-military samples from steps ii and iii. Parous CAF members were more likely to sustain repetitive strain injuries (overall and at the wrist and foot) compared to nulliparous peers. When matched for injury history, parous CAF members performed better in upper body muscular endurance, lower back muscular endurance, and lower body muscular strength. Conversely, the parous group in the non-military study participants demonstrated inferior performance in lower body power, lower back endurance, and lower body and upper body strength. Parity status should be considered independently and in conjunction with MSKi when developing support initiatives for females employed in both military and non-military contexts, however, given the opposing findings regarding fitness performance, research specific to the CAF is needed to inform policy and evidence-based practice.
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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 | 0.000 | 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 | 0.000 |
| 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".