Exploring human factors in the operating room: a protocol for a scoping review of training offerings for healthcare professionals
Bibliographic record
Abstract
INTRODUCTION: Applying human factors principles in surgical care has potential benefits for patient safety and care delivery. Although different theoretical frameworks of human factors exist, how providers are being trained in human factors and how human factors are being understood in vivo in the operating room (OR) remain unknown. The aim of this scoping review is to evaluate the application of human factors for the OR environment as described by education and training offerings for healthcare professionals. METHODS AND ANALYSIS: This scoping review will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. MEDLINE, Embase, PsycINFO, CINAHL, Health and Psychosocial Instruments and ERIC databases were searched on August 2020 from inception to identify relevant studies that describe the content, application and impact of human factors training for healthcare professionals or trainees who work in or interface with the OR environment. Titles, abstracts and full texts will be independently screened by two authors for eligible studies. Any disagreements will be resolved by discussion or by a third author when disagreement persists. Study information and training characteristics, such as the training tool used and type of learners and teachers, will be charted and summarised, and key themes in human factors training will be identified. Each training offering will be classified under the appropriate knowledge area(s) of human factors described by the Chartered Institute of Ergonomics & Human Factors (CIEHF). Themes that are not captured by the CIEHF framework will be independently recorded by two authors and included based on group discussion and consensus. ETHICS AND DISSEMINATION: Research ethics board approval is not required for this scoping review. The findings of this study will be disseminated at local and national conferences and will be published in a peer-reviewed journal.
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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 | 0.149 | 0.133 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.015 |
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".