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Record W4408683870 · doi:10.1016/j.admp.2025.102835

Entre normes et réalités : une analyse croisée de profils nationaux de SST

2025· article· fr· W4408683870 on OpenAlexaff
Rim El Kholti, P. Durand, Loubna Tahri, Fadwa Darid, A. El Kholti

Bibliographic record

VenueArchives des maladies professionnelles et de médecine du travail/Archives des maladies professionnelles et de l'environnement · 2025
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Les profils nationaux de la santé et la sécurité au travail (SST) sont des outils essentiels pour évaluer la gestion des risques professionnels à l’échelle nationale. Cette étude vise à comparer le profil national de la SST du Maroc à ceux de la Finlande et de la Tunisie. L’étude comparative des profils SST de la Finlande, de la Tunisie et du Maroc, basée sur l’Article 14 de la recommandation 197 de l’OIT, utilise les données disponibles et les estimations calculées à partir des chiffres de la Banque mondiale pour combler les lacunes. Cette méthodologie permet d’évaluer les systèmes nationaux et de formuler des recommandations. L’analyse comparative des profils nationaux de la SST met en évidence des différences significatives entre le Maroc, la Finlande et la Tunisie en termes de législation, de normes de SST, de mécanismes de coordination, de ressources financières et humaines, ainsi que de pratiques de prévention. L’analyse met en lumière les avantages et les défis de chaque profil national de la SST. La Finlande se distingue par son engagement envers la prévention, tandis que la Tunisie offre des avantages pour les organisations locales. Le Maroc a mis en place des dispositions légales pour promouvoir la SST, mais doit relever des défis spécifiques. Les profils nationaux SST du Maroc, de la Finlande et de la Tunisie présentent des différences significatives, mais offrent également des opportunités d’amélioration. Cette analyse contribue à l’avancement des connaissances en matière de SST et fournit des bases solides pour les décideurs politiques et les chercheurs dans le domaine de la santé et de la sécurité au travail. National OSH profiles are essential tools for assessing occupational risk management at the national level. This study aims to compare the national OSH profile of Morocco with those of Finland and Tunisia. The comparative study of the OSH profiles of Finland, Tunisia and Morocco, based on Article 14 of ILO Recommendation 197, uses available data and estimates calculated from World Bank figures to fill gaps. This methodology makes it possible to assess national systems and formulate recommendations. The comparative analysis of the national OSH profiles highlights significant differences between Morocco, Finland and Tunisia in terms of legislation, OSH standards, coordination mechanisms, financial and human resources, as well as prevention practices. The analysis highlights the advantages and challenges of each national OSH profile. Finland stands out for its commitment to prevention, while Tunisia offers advantages for local organizations. Morocco has legal provisions in place to promote OSH but faces specific challenges. The national OSH profiles of Morocco, Finland and Tunisia present significant differences, but also offer opportunities for improvement. This analysis contributes to the advancement of OSH knowledge and provides a solid foundation for policy makers and researchers in the field of occupational health and safety.

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0010.003
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.038
GPT teacher head0.386
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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