Assessment of the Physical Requirements Related to the Basic Training Program in Police Patrolling
Bibliographic record
Abstract
PURPOSE: To evaluate the demands of the basic training program in police patrolling (BTPPP) at the École Nationale de Police du Québec in order to develop a job-related physical test (JRPT). The aims were to: 1. quantify the physiological demands of the training and 2. identify and analyze the physically demanding and critical tasks of the training. METHODS: AIM #1: To determine the physiological demands, 56 cadets were recruited (27 M, 26 F). Their VO2max and maximal heart rate (HRmax) were directly assessed (VO2max : 48.9 ± 6.8 mlO2- kg-l·min-l) using an incremental treadmill test. The physiological demands of the BTPPP were later quantified by recording the HR of participants during the physically demanding classes and then VO2 was extrapolated using a personalised regression function. Video sequences were also taken during those classes to allow further analyses. AIM #2: To identify the critical tasks of the BTPPP, 12 police training-experts participated in an advisory activity in which they were asked to individually rate the critical aspect of various tasks using a seven point Likert-like scale. A ranking of the most critical physically demanding tasks was established based on the scores given by the experts. The tasks scored as the most critical were later analyzed by 4 experts in kinesiology in order to identify the physical abilities needed to execute those tasks. RESULTS: AIM #1: HR analysis showed participants spent very little (Avg. = 0.62%) of their time in class at a HR > 90 %; the most difficult classes required VO2 averaging only 35.2 mlO2- kg-l·min-l for females and 43.1 mlO2- kg-l·min-l for males. AIM #2: Critical tasks identification by police training-experts allowed the creation of a rank order list of 11 tasks of which the 7 most critical were, in order: reactive shooting, wrestling, self-defence with a baton, pursuing a suspect, force open a door, crowd control, and moving an unconscious person. Analysis of these 11 tasks by experts in kinesiology allowed the ranking of the most essential physical abilities, the first four being lower body power, coordination, upper body power and agility. CONCLUSION: The assessment of the physical demands of the BTPPP allowed the creation of a JRPT based on the proper abilities and tuned to the energy expenditure and critical physically demanding tasks taught during the training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".