Ageing and human-system errors in manufacturing: a scoping review
Bibliographic record
Abstract
Population ageing is acknowledged as a global trend affecting the manufacturing workforce. The progressive age-related decline of human capabilities may lead to an increase in human-system errors (HSEs) in production environments; hence, the study conducted a scoping literature review to determine the relationship between ageing and HSEs in manufacturing contexts. The review identified only 26 relevant studies, which showed that age is associated with HSEs in complex ways. With increasing age, the number, frequency, and probability of HSEs tend to increase, but this trend may be countered by experience effects. The review results suggested that it is necessary to consider the impact of ageing on operators’ performance. A theoretical framework for addressing the relationship between ageing and HSEs in manufacturing contexts was proposed. The framework highlighted the absence of studies reporting on perceptual, cognitive, and/or physical task demands, and the limited application of existing error typologies in the current literature. The results highlighted that important safety, productivity, and quality benefits accrue from paying attention to HSEs in system design and management, which should motivate further research in this area of growing importance.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".