Exploring the Arena of Work Disability Prevention Model for Stay at Work Factors Among Industrial Workers: A Scoping Review
Bibliographic record
Abstract
Abstract Objective The aging workforce influences employability and health of the working population, with new challenges emerging. The focus has shifted from return to work only, to enhancing ability to stay at work. It is unclear whether factors that influence return to work (RTW) also apply to preserving health and helping workers stay at work (SAW). Study objectives were to identify factors contributing to SAW among industrial workers and map identified factors to the Arena of Work Disability Prevention model (WDP-Arena, a commonly used RTW model) to identify agreements and differences. Methods Scoping review; eight databases were searched between January 2005- January 2020. Manuscripts with SAW as outcome were included; manuscripts with (early) retirement as outcome were excluded. Factors contributing to SAW were mapped against the components of the WDP-Arena. Results Thirteen manuscripts were included. Most results aligned with the WDP-Arena. These were most often related to the Workplace and Personal system. Compared to RTW, in industrial workers fewer factors related to the Legislative and Insurance system or Health Care system were relevant for SAW. Societal/cultural/political context was not studied. Multidimensional factors (workability, vitality at work, balanced workstyle, general health, dietary habits) were related to SAW but did not align with components in the WDP-Arena. Conclusion Most factors that determine SAW in industrial workers could be mapped onto the WDP- Arena model. However, new influencing factors were found that could not be mapped because they are multidimensional. The life-course perspective in SAW is more evident than in RTW. Many elements of the Legislative and Insurance system and the Health Care system have not been studied.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 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".