The influence of anthropometrics on physical employment standard performance
Bibliographic record
Abstract
BACKGROUND: The Canadian Armed Forces (CAF) recently implemented the Fitness for Operational Requirements of CAF Employment (FORCE), a new physical employment standard (PES). Data collection throughout development included anthropometric profiles of the CAF. AIMS: To determine if anthropometric measurements and demographic information would predict the performance outcomes of the FORCE and/or Common Military Task Fitness Evaluation (CMTFE). METHODS: We conducted a secondary analysis of data from FORCE research. We obtained bioelectrical impedance and segmental analysis. Statistical analysis included correlation and linear regression analyses. RESULTS: Among the 668 study subjects, as predicted, any task requiring lifting, pulling or moving of an object was significantly and positively correlated (r > 0.67) to lean body mass (LBM) measurements. LBM correlated with stretcher carry (r = 0.78) and with lifting actions such as sand bag drag (r = 0.77), vehicle extrication (r = 0.71), sand bag fortification (r = 0.68) and sand bag lift time (r = -0.67). The difference between the correlation of dead mass (DM) with task performance compared with LBM was not statistically significant. CONCLUSIONS: DM and LBM can be used in a PES to predict success on military tasks such as casualty evacuation and manual material handling. However, there is no minimum LBM required to perform these tasks successfully. These data direct future research on how we should diversify research participants by anthropometrics, in addition to the traditional demographic variables of gender and age, to highlight potential important adverse impact with PES design. In addition, the results can be used to develop better training regimens to facilitate passing a PES.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".