Does a history of childbirth impact injury prevalence and mental health in female military members?
Bibliographic record
Abstract
The effect of parity status on the prevalence and impact of musculoskeletal injury (MSKi) among female Canadian Armed Forces (CAF) members is unknown. This study aims to identify whether a history of childbirth and pregnancy-related complications are associated with MSKi occurrence among female members of the CAF. From September 2020 to February 2021, data were collected via an online questionnaire that assessed MSKi, reproductive health, and barriers to recruitment and retention in the CAF. Actively serving, female members were included in this analysis stratified by parous ( n = 313) or nulliparous ( n = 435) status. Descriptive analysis and binary logistic regressions were used to identify prevalence and adjusted odds ratios (aOR) of repetitive strain injuries (RSI), acute injuries, and body regions affected. Covariates included in aOR: age, body mass index, and rank. A p value of <0.05 was considered significant and 95% confidence intervals (CI) were reported. Female members with a history of childbirth were more likely to report an RSI (80.9% vs. 69.9%, OR = 1.57, CI: 1.03 to 2.40), and when stratified by body region, were more likely to have an RSI of the wrist (30.0% vs. 20.5%, aOR = 1.62, CI: 1.09 to 2.40), and foot (39.3% vs. 24.1%, aOR = 1.79, CI: 1.24 to 2.59). When compared to the nulliparous group, parity did not influence prevalence of acute injuries. MSKi and mental health perceptions were different for females who experienced postpartum depression, miscarriage, or preterm birth. Childbirth and pregnancy-related complications impact prevalence of some RSI among female CAF members. Thus, specific health and fitness support may be needed for parous female CAF members.
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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 | 0.000 | 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".