Police Officer Discomfort and Activity Characterization During a Day Shift and a Night Shift
Bibliographic record
Abstract
The purpose of this study was to identify occupational and car seat features causing discomfort in patrol officers, and to determine which body parts were experiencing the most discomfort. A Seat Features and Occupational Components Questionnaire, based on a 0 to 100 mm Visual Analog Scale (VAS), revealed that the duty belt was the occupational gear causing the most discomfort, followed by computer use within the car. The seat lumbar support was the seat feature causing the most discomfort. A Body Part Discomfort Questionnaire was administered at the beginning of the shift (T1), after six hours (T6), and at the end of the twelve hour shift (T12), for both day and night shifts. There were no significant differences in body part discomfort between the two types of shifts. There were, however, significant increases in body part discomfort ratings over the course of the working day, especially on the right side of the body. While some body parts experienced a significant increase in discomfort between the T1 and T6 (i.e., the neck, left upper back, right buttocks), some body parts only had a significant increase in discomfort after six hours (i.e., the lower back and mid back). The two body parts that experienced the highest levels of discomfort were the neck and lower back. A secondary purpose of the study was to identify the frequency of the activities that occur within the car. The largest portion of the workday and night were spent outside of the vehicle (46.1±10.8 % during the day, and 43.5±14.9% during the night). Left-handed driving occupied the most time in the car (26.3±10.1% during the day, and 25.7 ± 8.6% at night). A reduced or reconfigured duty belt, as well as decreased time spent in the car (doing paper work, computer work, and driving), could help decrease discomfort levels.
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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 | 0.000 | 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 | 0.001 | 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".