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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".