Under pressure: live observation of ergonomic challenges in the operating room
Bibliographic record
Abstract
Ergonomics is the study of how people interact with their environment to improve efficiency and well-being. In the operating room (OR), poor ergonomic practices are common and contribute to musculoskeletal (MSK) injuries, leading to absenteeism, changes in clinical practice, burnout, and potentially compromised patient care1–3. Ergonomic principles are rarely applied in the OR, due to limited awareness, education, support, and tools2. We aimed to identify ergonomic risks and challenges in the OR through live observation, to inform the development of targeted education for safer OR work. We conducted a cross-sectional observational study over three days with live observations across diverse surgical procedures (open and laparoscopic hepatectomy and pancreatectomy, lower extremity melanoma excision with sentinel node biopsy, posterior craniotomy, and open cervical and lumbar laminectomy). Two independent ergonomists and chiropractors conducted semi-structured assessments of OR workers, the environment, and their interactions using a data collection form developed for this study. Observation sheets and transcripts were analysed using the Systems Engineering Initiative for Patient Safety 2.0 (SEIPS) framework4. SEIPS 2.0 is a human factors model that examines how work system elements interact to influence processes and healthcare outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".