Ergonomía participativa: empoderamiento de los trabajadores para la prevensión de trastornos musculoesqueléticos
Bibliographic record
Abstract
Participatory ergonomics is an intervention strategy acting on physical load exposures occurring in occupational settings, scarcely known in Spain but with a number of experiences and evidences coming from other countries. There are several reasons justifying the interest of this approach. First, participatory ergonomics focuses on one of the categories of occupational exposures with the largest impact on workers' health in a majority of countries all over the world, in terms of incidence, prevalence and disability. Secondly, basic principle in participatory ergonomics is empowerment of workers for them to participate identifying risks and injuries caused by physical exposures at work as well as proposing and evaluating proper control measures for each situation. Thirdly, it allows dealing and solving a number of problems without the use of complex technical protocols. From a public health perspective, participatory ergonomics is a largely tried model of community empowerment for the control of (occupational) factors affecting health and wellbeing. In this paper we revise some basic principles of participatory ergonomics, we comment on the keys leading to success or failing of the interventions and we present some main results coming from participatory ergonomics experiences developed for a long time in countries such as Canada, United Kingdom, Netherlands or Finland.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".