Predicting casualty evacuation performance for the Canadian land forces command
Bibliographic record
Abstract
Background: Recent observations and feedback from military operations demonstrated the need to reassess the validity of a fireman's carry (FC) as an assessment of casualty evacuation ability in the Canadian land forces command physical fitness standard. Objective: The objective of this research was to determine: (1) the most common methods of casualty evacuation employed by the Canadian land forces (LF), (2) performance of land forces personnel on casualty evacuation abilities, and (3) potential predictive fitness tests (PFT) for performance on a casualty evacuation task. Methods: Task analysis, interviews, observations, and subject matter experts were used to identify the common casualty evacuation methods. Simulations of these methods were developed and validated. LF members performed these evacuation methods and 7 PFT to their maximum ability (N=118). These data were analyzed using step-wise regression analysis, ANOVA and Pearson product moment correlations. Results: Casualty drag (CD) and vehicle extrication (VE) were found to be the most common methods of casualty evacuation. Males performed significantly better on VE and CD compared to females. CD predicted VE, and the addition of grip strength and static squat performance improved the prediction by 26% to account for 65% of the variability in VE. Of the LF members tested, 88% were able to drag an 82 kg casualty 25 m and 83% succeeded in extricating an 82 kg casualty from a light armored vehicle. Conclusions: It is recommended that the FC be replaced by a 25 m CD. It is not recommended that VE be tested, as it is expected that VE of an 82 kg casualty could be performed by two soldiers; all subjects capable of dragging an 82 kg casualty could also extricate 41 kg.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".