Contribution of universities health and safety services in achieving the sustainable development goals
Bibliographic record
Abstract
Introduction: Universities play a pivotal role in advancing the Sustainable Development Goals (SDGs) and the 2030 Agenda through their educational mission, research activities, and contributions to social well-being, economic progress, and social cohesion. Beyond their conventional functions, universities can contribute to sustainability through the specific actions of their Occupational Health and Safety (OHS) services. This study explores the contribution of university OHS services to the achievement of the SDGs within the Spanish higher education system. Methods: A cross-sectional survey was conducted among Spanish universities to identify OHS actions linked to the SDGs and to assess their institutional impact. The study presents a structured methodology that classifies OHS actions into specific and general categories, mapped across three strategic dimensions: person, environment, and culture. Results: Results reveal that OHS services contribute to a wide range of SDGs, extending beyond the traditionally recognized SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth). Discussion: This research provides an original SDG Impact Matrix that highlights the multidimensional role of OHS services in fostering sustainability within universities. The findings offer valuable insights for integrating OHS strategies into institutional sustainability policies and expanding their role as active agents in the advancement of the 2030 Agenda.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".