Association Between Reproductive Health Factors and Musculoskeletal Injuries in Female Canadian Armed Forces Members
Bibliographic record
Abstract
Background: Musculoskeletal injuries (MSKi) play a role in member retention in the military. In general, female military members have higher rates of MSKi than males and female reproductive health characteristics may be contributing to these disparities. This study seeks to characterize reproductive health factors in female Canadian Armed Forces (CAF) members and their relationship with MSKi. Materials and Methods: An electronic survey (SurveyMonkey ® ) was made available to present and former CAF members 18–65 years of age. Responses were collected between September 2020 and February 2021. Seven female reproductive characteristics were assessed: age of menarche, menstrual cycle regularity, birth control use, having given birth while serving, endometriosis, early menopause, and secondary oligomenorrhea/amenorrhea. Binary logistic regressions were used to analyze associations between reproductive characteristics with repetitive strain (RSI) and acute injuries. Results: A total of 2,001 participants consented to the survey with 855 respondents being female. Females reporting menstrual cycles as never regular, irregular for a few months, who never had a period, and whose periods stopped while serving presented a greater likelihood of reporting RSI compared to their peers who reported regular menstrual cycles (adjusted odds ratio [aOR]: 1.898, confidence interval [CI]: 1.138–3.166). Participants who have experienced endometriosis presented a higher likelihood of reporting acute injuries than those who did not (aOR: 2.426, CI: 1.030–5.709). Conclusion: This examination of females within the CAF suggests that irregular menstrual cycles or absent periods increase the likelihood of experiencing MSKi, namely those categorized as RSI injuries and reporting endometriosis were associated with greater rates of acute injuries.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".