Interdisciplinary Operating Room Ergonomics Needs and Priorities
Bibliographic record
Abstract
OBJECTIVE: To examine perceived operating room (OR) ergonomics facilitators and barriers, with a focus on the interdisciplinary team. BACKGROUND: Poor ergonomics causes musculoskeletal injuries affecting all OR staff with repercussions on patient care, outcomes, and sustainability. Lack of ergonomic awareness and education are risk factors. METHODS: We conducted a self-administered web-based survey of OR nurses, surgeons, and anesthesiologists at a single center (n = 238). We developed a questionnaire through item generation and reduction, followed by reliability and validity testing. RESULTS: Response rate was 53.8%. Respondents perceived that on average 80% of nurses, 70% of surgeons, and 40% of anesthesiologists experienced musculoskeletal (MSK) injuries, with no difference in professional groups' perceptions. Guideline ergonomics interventions were rarely used (<25%) except for specialized clothing (33%), equipment repositioning (59%), and seating (37%), though perceived as beneficial by 80% to 90%. Reported barriers to optimal ergonomics were organizational/structural (lack of time, space, equipment, funding), whereas solutions were individual. Fear of unfavorable perceptions from others was a concern for 62%. Teams discussing, prioritizing, monitoring, or helping with ergonomics was indicated by <50%. Individual ergonomic adaptations were perceived as convenience by other staff. CONCLUSIONS: While structural/organizational issues are reported as barriers to ergonomics, solutions appear as individual responsibilities. Team dynamics did not prioritize nor support ergonomics. Education tools leveraging the interdisciplinary team are warranted. This work will be supplemented by interviews and live observations to build tailored educational tools for OR teams.
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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 | 0.004 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".