Postpartum Women’s Perceptions of Risk of Musculoskeletal Injuries in the Canadian Armed Forces: A Qualitative Research Study
Bibliographic record
Abstract
Musculoskeletal injuries (MSKi) are a major concern within military forces, significantly reducing productivity and military readiness. Within the Canadian Armed Forces (CAF), MSKi are the most common cause of delayed deployment of members. There is a lack of research specifically focused on the experiences of postpartum CAF members and their perceived risk of MSKi. Drawing on Giles et al.’s equity-centered 4 E’s injury prevention framework (education, engineering, enforcement, and equity), we highlight that individuals who experience pregnancy may perceive themselves to be at heightened risk of injury due to sex and gender-based inequities in their workplace. This qualitative research draws on data from focus groups with 32 individuals who experienced pregnancy while serving in the CAF. Using reflexive thematic analysis, we identified the following findings related to perceived increased risk of MSKi: (a) nature of relevant physiological and anatomical changes in pregnancy, (b) unreasonable pressures to return to work at peak physical readiness, and (c) perceived challenges associated with accessing resources and services to support physical recovery. There are opportunities to improve access to injury prevention resources and support for pregnant and postpartum CAF members to reduce rates of MSKi. Findings from this study may be additionally relevant to armed forces more broadly or other professions that require return to physical readiness.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".