Reorganizing Nursing Work on Surgical Units: A Time-and-Motion Study
Bibliographic record
Abstract
A time-and-motion study was conducted in response to perceptions that the surgical nursing staff at a Montreal hospital was spending an excessive amount of time on non-nursing care. A sample of 30 nurse shifts was observed by trained observers who timed nurses' activities for their entire working shift using a hand-held Personal Digital Assistant. Activities were grouped into four main categories: direct patient care, indirect patient care, non-nursing and personal activities. Break and meal times were excluded from the denominator of total worked hours. A total of 201 working hours were observed, an average of 6 hours, 42 minutes per nurse shift. The mean proportions of each nurse shift spent on the main activity categories were: direct care 32.8%, indirect care 55.7%, non-nursing tasks 9.0% and personal 2.5%. Three activities (communication among health professionals, medication verification/preparation and documentation) comprised 78.9% of indirect care time. Greater time on indirect care was associated with work on night shifts and on the short-stay surgical unit. Subsequent work reorganization focused on reducing time spent on communication and medications. The authors conclude that time-and-motion studies are a useful method of monitoring appropriate use of nursing staff, and may provide results that assist in restructuring nursing tasks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| 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".