Analyzing what nurses do during work in a hospital setting: A feasibility study using video
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Patient transfers have been implicated as a contributing factor in the high work-related musculoskeletal disorder (MSD) rate in nursing. However, documenting how much time is spent doing such tasks, compared to other less biomechanically stressful tasks in the workplace, has been limited, and not performed to date using a video-based approach. Therefore, the purpose of this study was to determine the feasibility of documenting all job-related nursing tasks performed during a typical shift in a hospital setting using video. PARTICIPANTS: Ten female nurses from an acute care hospital who worked in different units and during all three shifts. METHODS: Nurses working in different units of the hospital were videotaped performing their normal job-related tasks for a 2 hour period. Video records were subsequently analyzed to identify and categorize all tasks performed by each nurse. RESULTS: Overall, nurses spent less than 7% of their time during patient moving and transfer activities. One third of their time was spent walking, standing and sitting, 19.8% charting, 14.7% in patient care, 13.9% preparing medicines, 9.5% in housekeeping, and about 3% in self-care. CONCLUSIONS: This study showed that video-based methods are feasible for documenting what nurses do in the workplace. It also highlighted the diversity and non-repetitive nature of the workplace tasks nurses perform and suggests that ergonomic assessments of the cumulative effects of work on nurses in the field should focus on more than just patient handling activities.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it