Identification and characterization of the critical physically demanding tasks encountered by correctional officers
Bibliographic record
Abstract
The purpose of this investigation was to identify the critical tasks encountered by correctional officers (COs) on the job and to conduct a comprehensive assessment and characterization of the physical demands of these tasks. These are the first steps in developing a fitness screening test for COs in compliance with recent legislation. The most important, physically demanding, and frequently occurring tasks were identified using Delphi methodology, focus groups, and questionnaire responses from 190 experienced front-line COs. These tasks were structured into emergency response scenarios for which a physical and physiological characterization was conducted to verify their relative physical demands analysis. Oxygen consumption and the forces exerted by COs were quantified while they were responding and then controlling and restraining inmates. The female COs used less force than the male COs did to control and restrain the same inmates (body control = 46 vs. 60 kg, wrist hold = 32 vs. 49 kg, and arm retraction = 37 vs. 47 kg) and did not exert their maximal strength during their control and restraint activities. The mean oxygen consumption of the female and male COs while performing the on-the-job tasks was similar (39.5 vs. 38.5 mL.kg-1.min-1). We concluded that the essential components of a fitness screening protocol for CO applicants are cell search, expeditious response, body control, arm restraint, inmate relocation, and an assessment of aerobic fitness. The criterion performance standards for completing these tasks in a circuit were set at the job performance level of safe and efficient female COs.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| 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".