P115 Exploring the effectiveness of Fatigue Risk Management Systems
Bibliographic record
Abstract
Abstract Introduction Fatigue Risk Management Systems (FRMS) are increasingly being used in a range of safety-critical industries to manage fatigue-related risk. These data-driven practices use a risk-based approach to managing fatigue, rather than solely relying on prescriptive hours of work limits. However, some organisations are reluctant to implement FRMS as evidence of effectiveness is unclear. A review was undertaken to evaluate the available evidence on FRMS effectiveness, and to provide evidence-based policy guidance for FRMS use and implementation. Methods Seven electronic databases (MEDLINE (Ovid), PsycINFO, IEEE Xplore Digital Library, Scopus, Web of Science, MESH Occupational Health, NIOSHTIC II) were searched for relevant peer reviewed and grey literature. Documents were included if they addressed fatigue management interventions. Screening was performed on a total of 2129 documents, with 231 included in the final review. Results Few (n = 5) documents evaluated FRMS as a whole. However, components of FRMS (e.g., performance monitoring, fatigue detection technology, prior sleep wake behaviour assessment) appeared to improve key safety and fatigue outcomes. Discussion The effectiveness of FRMS components suggests that FRMS as a whole is likely to improve organisational safety outcomes. Key implementation enablers included organisational and worker commitment, and safety culture. Where organisations have limited resources and/or immature safety cultures, FRMS may not be appropriate. As such, we propose that a hybrid or transitional model of fatigue management (including risk-based and prescriptive components) could be implemented – so organisations can increase operational flexibility while improving safety.
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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 | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".