Promoting Health and Safety Culture at Work Through Workforce Engagement
Bibliographic record
Abstract
Abstract Saipem celebrates the WORLD DAY OF SAFETY AND HEALTH AT WORK every April 28. It is an initiative promoted by ILO (the International Labour Organization) to encourage the prevention of work-related accidents and disease on a global scale. In honour of the occasion, the company organises a series of exciting activities that enable the direct involvement of all employees, encouraging genuine Team Building and raising awareness on the well-being of the individual. This year, in line with the theme proposed by ILO for 2013 - occupational diseases and general health, Saipem's LiHS (Leadership in Health and Safety) Team decided to focus on a new, spectacular and effective formula for stimulating the direct participation of the company: the Health Prevention Flash Mob. All participants were given free rein to design and carry out their projects, ranging from the collective launch of a slogan to creating an unprecedented poster, from the organisation of a special dance to the preparation of an original symbolic action. The challenge, though ambitious, was greeted with excitement and enthusiasm in many of the Saipem offices and sites around the world (Indonesia, Nigeria, Canada, America, Colombia, UK, UAE, Angola, Italy, Romania and Saipem vessels), who got hundreds of people motivated to create exceptional events, all expressing a universal message of health through the local culture. The company intranet, e-mails and word of mouth meant that these projects soon went viral, both inside and outside the company, maximising the focus on the prevention of occupational diseases in the most compelling manner. Through the promotion and dissemination of the first ever flash mobs dedicated to safety at work, Saipem has become a pioneer of innovative Health & Safety communications, proof of the start of a true cultural change.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.026 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".