Economic evaluation of ergonomic solutions: Part I – Guidelines for the practitioner * *The recommendations provided in this guide are based on numerous published and unpublished scientific studies and are intended to enhance worker safety and productivity. These recommendations are neither intended to replace existing standards, if any, nor should be treated as standards. Furthermore, this document should not be construed to represent institutional policy.The following individuals participated in the discussion of the earlier version of this guide. Their suggestions (written or verbal) were incorporated by the authors in this version: Alvah Bittner, USA; Peter Buckle, UK; Jan Dul, The Netherlands; Bahador Ghahramani, USA; Juhani Ilmarinen, Finland; Sheik Imrhan, USA; Asa Kilbom, Sweden; Shrawan Kumar, Canada; Tom Leamon, USA; Mark Lehto, USA; William Marras, USA; Barbara McPhee, Australia; James Miller, USA; Anil Mital, USA; Don Morelli, USA; Maurice Oxenburgh, Australia; Jerry Purswell, USA; Jorma Saari, Finland; W. (Tom) Singleton, UK; Juhani Smolander, Finland; Terry Stobbe, USA; Rolf Westgaard, Norway; Jørgen Winkel, Sweden. The guide was also reviewed in depth by several anonymous reviewers.
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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 | 0.051 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".