Modelling physical fatigue and recovery patterns within and across multiple shifts using a healthcare example
Bibliographic record
Abstract
Work induced fatigue is associated with quality deficits, human-system errors, accidents, and injuries in the workplace, which in many industries may cause harm to employees and communities they serve. Managing physical fatigue requires tools that inform system design and management decisions. This research presents a method to quantify employee fatigue levels across multiple days. Physical workload time history from simulation provides inputs to empirically based endurance time and recovery models. Outputs include fatigue level indicators within and across multiple shifts. Three nursing example scenarios demonstrate the recovery needed over a two-week shift schedule in response to: (1) varying recovery efficiency between shifts; (2) nurse strength differences; and (3) shift scheduling changes. Scenarios showed the impact of fatigue accumulation and time spent needing recovery or time fully recovered due to competing interests outside of work, individual differences, as well as organisational influences on work scheduling. While further research is needed, the proposed method can provide work system designers and managers with critical information about employee fatigue consequences for operational and design decisions in work systems. This information is crucial for the design of safe, effective, and sustainable high-quality performance in Industry 5.0 systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".