Determining the usability and technology acceptance of a powered and automated cargo management system during ladder lifting tasks: A pilot study
Bibliographic record
Abstract
BACKGROUND: Evidence for the adoption and acceptance of assistive devices for ladder lifting tasks by workers is scarce. OBJECTIVE: This study aims to investigate the technology acceptance and usability of a powered and automated cargo management system (RazerLift®) used by workers who need to lift ladders as part of their daily duties, as compared to mechanical cargo management systems (traditional). METHODS: We used a one-way repeated measures design in this study. Our primary outcome variable was a usability performance measurement measured as time (in seconds) for unloading and loading ladders using both systems. Our secondary outcome was technology acceptance, measured using questionnaires with a 5-point Likert scale: "strongly disagree (1)" to "strongly agree (5)". RESULTS: The participants conducted the combined unloading and loading time using the powered and automated system (RazerLift®) 20.85 seconds faster than the traditional system (p-value = 0.000, t-value (df) = -5.730 (6), d = 2.713). Overall, the RazerLift® system (mean = 44.28, SD 5.58) had a higher technology acceptance compared to the traditional system (mean = 30.00, SD 7.91), (p = 0.041, t-value (df) = 6.589 (6), d = 4.60). CONCLUSIONS: The RazerLift® was more time efficient compared with the traditional system, and (2) the RazerLift® was superior in terms of technology acceptance compared to the traditional system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".