Bibliographic record
Abstract
BACKGROUND: Staff in hospitals frequently travel between floors and choose between taking the stairs or elevator. We compared the time savings with these two options. METHODS: Four people aged 26-67 years completed 14 trips ranging from one to six floors, both ascending and descending. We compared the amount of time per floor travelled by stairs and by two banks of elevators. Participants reported their fatigue levels using a modified Borg scale. We performed two-way analysis of variance to compare the log-transformed data, with participant and time of day as independent variables. RESULTS: The mean time taken to travel between each floor was 13.1 (standard deviation [SD] 1.7) seconds by stairs and 37.5 (SD 19.0) and 35.6 (SD 23.1) seconds by the two elevators (F=8.61, p<0.001). The difference in time taken to travel by stairs and elevator equaled about 15 minutes a day. Self-reported fatigue was less than 13 (out of 20) on the Borg scale for all participants, and they all stated that they were able to continue their duties without resting. The extra time associated with elevator use was because of waiting for its arrival. There was a difference in the amount of time taken to travel by elevator depending on the time of day and day of the week. INTERPRETATION: Taking the stairs rather than the elevator saved about 15 minutes each workday. This 3% savings per workday could translate into improved productivity as well as increased fitness.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.016 |
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".