Decentralisation of the Compliance of Anti-tobacco Law in India: The Case of Higher Educational Institutions in New Delhi, India
Notice bibliographique
Résumé
Introduction This paper studies the decentralised compliance responsibility of India's tobacco control legislation, called the Cigarettes and Other Tobacco Products (Prohibition of Advertisement and Regulation of Trade and Commerce, Production, Supply and Distribution) Act of 2003, with its rules, which the government has outsourced to higher educational institutions, studied through an example of New Delhi. The study looks into the three most important parameters of decentralised compliance. Two of these require the installation of signboards by higher educational institutions, and the third involves imposing and collecting fines against persons found smoking within the educational institutions. Regarding the boards, the first board is about the warning prohibiting the sale within 100 yards of educational institutions, and the second one prohibits smoking in educational institutions. The study also checks with the educational institutions whether there is a presence of cigarette and tobacco product vendors within 100 yards of the institution, where the sale of such products has been banned by law. The study also found educational activities to create awareness in the institutions for tobacco control and cessation. Methods Thirty-six higher educational institutions, including universities, were randomly selected and studied through a unique methodology using India's transparency law, i.e., the Right to Information Act 2005. The portions of the law, which was direct compliance, or related compliance by the higher educational institution was included in the study. This justified the decentralised responsibility of these higher educational institutions. Applications for information under the transparency law were requested and supplied. Out of the 47 institutions, in which information requests were filed, 36 provided the information under the law. Credible information was provided by the higher educational institutions and this information was collated and interpreted to provide insights into the compliance by the higher educational institutions. Results Only 44.4% of the institutions had a board prohibiting the sale of cigarettes and other tobacco products. This was a non-universal compliance by the higher educational institutions. 88.9 per cent had boards prohibiting smoking in the higher educational institutions. Only one out of the 36 had an instance of smoking recorded and collection of fines. While 47.2% reported to not have any instance of smoking and fine collection. Fifty per cent of the institutions had no record of fine collection amounting to defiance of the law. 75 per cent of institutions did some kind of awareness activities which was not a direct statutory requirement, but a recommended guideline. Conclusion The results show that the intent to decentralise the compliance of the tobacco control law in New Delhi by the higher educational institutions has not been universally successful and requires much effort for its on-ground penetration. Such studies have a policy impact as they serve as an example for the generalisability of such statutes, which only work when there is implementation from the bottom-up and when the deterrent is also strong with incentivisation to the educational institutions to implement tobacco control with vigour.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».