Determination of Orientation and Practice Requirements When Using an Obstacle Course for Mobility Performance Assessment
Bibliographic record
Abstract
OBJECTIVE: Determine effect of orientation (introduction and familiarization) and practice (repeated performance) on human performance under various load conditions as assessed by an obstacle course. BACKGROUND: Obstacle courses are commonly used as screening tools by military, police, and firefighters or to assess human capabilities and the effect of wearing personal protective equipment (PPE) and other occupationally necessary equipment on mobility task performance. Unfortunately, little is formally documented about the effect of orientation and practice on performance outcomes of obstacle or mobility courses being used. METHOD: Forty-eight participants were recruited from the Canadian Army Infantry and Combat Engineer population. Participants either received regular or extensive orientation of the course before completing it. Following orientation, participants completed the course five consecutive times while wearing their PPE with full fighting order (FFO) and five consecutive times while wearing no PPE and non-FFO across a five-day period (maximum two runs per day), with ensemble presentation order counterbalanced. Total course completion time and individual obstacle completion times were measured for each run of the course. RESULTS: While wearing FFO, participants continued to decrease the time required for completing the course; however, while wearing non-FFO, time to course completion did not significantly change over the five runs. There were no differences in course completion times for the regular and extensive course orientation groups. CONCLUSIONS: Considerations required to mitigate orientation and practicing effects can differ depending on type or complexity of load condition. While wearing FFO, practicing effects can introduce undesired confounding factors into data collection. APPLICATION: Any practice runs on an obstacle course prior to its use as an assessment tool should focus on the loaded (e.g., FFO) condition because improvement on loaded runs is likely transferred to unloaded, but this does not apply in the reverse.
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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 | 0.003 | 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 | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".