Exploring human factors in the operating room: scoping review of training offerings for healthcare professionals
Bibliographic record
Abstract
BACKGROUND: Human factors (HF) integration can improve patient safety in the operating room (OR), but the depth of current knowledge remains unknown. This study aimed to explore the content of HF training for the operative environment. METHODS: We searched six bibliographic databases for studies describing HF interventions for the OR. Skills taught were classified using the Chartered Institute of Ergonomics and Human Factors (CIEHF) framework, consisting of 67 knowledge areas belonging to five categories: psychology; people and systems; methods and tools; anatomy and physiology; and work environment. RESULTS: Of 1851 results, 28 studies were included, representing 27 unique interventions. HF training was mostly delivered to interdisciplinary groups (n = 19; 70 per cent) of surgeons (n = 16; 59 per cent), nurses (n = 15; 56 per cent), and postgraduate surgical trainees (n = 11; 41 per cent). Interactive methods (multimedia, simulation) were used for teaching in all studies. Of the CIEHF knowledge areas, all 27 interventions taught 'behaviours and attitudes' (psychology) and 'team work' (people and systems). Other skills included 'communication' (n = 25; 93 per cent), 'situation awareness' (n = 23; 85 per cent), and 'leadership' (n = 20; 74 per cent). Anatomy and physiology were taught by one intervention, while none taught knowledge areas under work environment. CONCLUSION: Expanding HF education requires a broader inclusion of the entirety of sociotechnical factors such as contributions of the work environment, technology, and broader organizational culture on OR safety to a wider range of stakeholders.
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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".