1711d The place of cooperation in the evolution of occupational medicine in tunisia
Bibliographic record
Abstract
Since its independence in 1958, Tunisia never ceased confirming its political will to protect worker’s health by issuing successive and specific legislative and regulatory texts. However, the achievement of this objective didn’t really arise until 1978 thanks to the establishment of the training course for occupational medicine specialists at the faculty of medicine of Tunis, after benefiting from north-south cooperation programs particularly with France, Belgium and Canada. That’s how we have now 40 specialised instructors (Prof, MCA, AHU) distributed in four medical schools in Tunisia, 50 labour doctor inspectors, 60 labour medical advisors at the CNAM and 1000 occupational doctors in different companies. Furthermore, the country has a research facility specialised in industrial toxicology, an occupational health and safety institute and a national council for preventing professional hazards which gathers all contributors in the field of occupational health and safety. As a consequence, occupational medicine in Tunisia got through its creation stage and is currently oriented towards promoting local and international cooperation in many fields, particularly in multilateral research and in enhancing specific axes of the specialty.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".