Evolution of human factors research in aviation safety: A systematic review and bibliometric analysis of the intellectual structure
Bibliographic record
Abstract
Despite the multitude of research endeavors dedicated to Human Factors (HF) in aviation safety, a comprehensive review remains conspicuously scarce. Accordingly, this study presents the first in-depth systematic review and bibliometric analysis of the vital role played by HF in enhancing the safety and reliability of air transportation. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, we scrutinized the Scopus dataset spanning from 1937 to late 2023. A rigorous screening process was applied to identify relevant documents, ultimately subjecting critical analyses of 1663 documents to address four foundational research questions within HF associated with aviation safety. First, our analysis delves into the identification of key areas of emphasis that have characterized HF in the aviation industry since 1937. By tracing the trajectory of research over time, the study aims to discern the evolution of HF within the aviation context. Furthermore, an exploration of primary challenges and knowledge gaps crucial to research is highlighted, with proposed pathways for future investigations to maximize their impact on air transportation safety. Finally, the study extends its inquiry to compare the existing landscape of human reliability research within the aviation sector with that of Nuclear Power Plants (NPPs) and the Chemical Process Industry (CPI). This holistic approach to understanding HF not only contributes valuable insights into aviation safety but also contextualizes these findings within broader industrial frameworks, revealing the key gaps that exist in human reliability within the aviation industry. The outcomes of this study underscore the indispensable role of HF in establishing and advancing safer and more resilient air transportation systems.
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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 | 0.053 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.152 | 0.152 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".