Systemic factors in workplace accidents: An umbrella review of series injuries and fatalities
Bibliographic record
Abstract
Increasingly the complexities of organisational systems and the systemic nature of serious workplace injuries and fatalities are characterised by unpredictable outcomes. A complex systems thinking approach is needed to identify and analyse system interactions and systemic causal factors to explore the connections between people, technology and organisational systems and the incidence of workplace accidents. This umbrella review aimed to analyse the peer-reviewed systematic review literature related to accident causation for serious workplace accidents and fatalities to develop an understanding of the systemic causal factors across a range of complex systems. Some electronic databases were searched using the key search terms “accident causation factors”, “accident∗”, “fatalit∗”, “work∗” and their variations between 2000 and 2022. The selected papers underwent screening, eligibility and quality appraisal process using checklist for systematic reviews and research syntheses. Following the systematic process, a total of 13 papers were included in the total data set. The studies originated from a variety of workplaces such as industry, aviation, mining, maritime and construction industries. The most common contributing factors were sleep deprivation, fatigue and substance abuse whereas organisational factors such as management systems, resources and equipment as resulting in significant workplace incidents. This review identified that human errors and organisational and system factors were the main source of accident causation across multiple industries. The importance of learning from incidents and need for more sophisticated reporting systems was identified as being essential to change workplace culture and safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".