Low physical fitness indicates future injury, mental health, menstrual cycle disruptions, and burnout in female emergency service personnel and healthcare providers
Bibliographic record
Abstract
Musculoskeletal injury (MSKi), depression, anxiety, and burnout place a considerable burden on emergency services personnel and healthcare providers (HCP). Physical fitness is related to both mental and physical health in these populations, but females in these are hugely underrepresented in this literature. As female representation in first-responder and HCP roles increases, the need for female-specific research is needed. This study examines physical fitness as a short-term indicator of future reproductive health, MSKi, and mental health for females employed as first-responders or HCP. Thirteen first-responders and 29 HCP completed an initial health and demographics questionnaire, a comprehensive physical assessment (e.g., bone mineral density, muscular strength, muscular endurance, muscular power, flexibility, and aerobic capacity), and a health questionnaire 6-7 months after the physical testing. We found that (i) bone mineral density, relative upper body strength, and lower body power were related to sustaining future MSKi, (ii) better lower body endurance and flexibility were related to future menstrual cycle disruptions, and (iii) low bone mineral density was related to future self-reported burnout and Patient Health Questionnaire score ≥ 10. Physical fitness characteristics can be helpful indicators of future MSKi risk, menstrual cycle disruptions, and mental health status in females employed in arduous occupations.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".