Analyzing what nurses do during work in a hospital setting: A feasibility study using video
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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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".