Background: This paper presents the student outcomes of a large-scale, social-influences-based, school and media-based tobacco use prevention and cessation project in Southern California.
Methods: The study provided an experimental comparison of classroom delivery with television delivery and the combination of the two in a 2 x 2 plus 1 design. Schools were randomly assigned to conditions. Control groups included "treatment as usual" and an "attention control" with the same outcome expectancies as the treatment conditions. Students were surveyed twice in grade 7 and once in each of grades 8 and 9. The interventions occurred during grade 7.
Results: We observed significant effects on mediating variables such as knowledge and prevalence estimates, and coping effort. The knowledge and prevalence estimates effects decayed partially but remained significant up to a 2-year follow-up. The coping effort effect did not persist at follow-ups. There were significant main effects of both classroom training and TV programming on knowledge and prevalence estimates and significant interactions of classroom and TV programming on knowledge (negative), disapproval of parental smoking, and coping effort. There were no consistent program effects on refusal/self-efficacy, smoking intentions, or behavior.
Conclusions: Previous reports demonstrated successful development and pilot testing of program components and measures and high acceptance of the program by students and parents. The lack of behavioral effects may have been the result of imperfect program implementation or low base rates of intentions and behavior.
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http://dx.doi.org/10.1006/pmed.1995.1005 | DOI Listing |
Exp Appl Acarol
January 2025
Laboratorio de Vectores y Enfermedades Transmitidas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Salto, Uruguay.
Babesia species (Piroplasmida) are hemoparasites that infect erythrocytes of mammals and birds and are mainly transmitted by hard ticks (Acari: Ixodidae). These hemoparasites are known to be the second most common parasites infecting mammals, after trypanosomes, and some species may cause malaria-like disease in humans. Diagnosis and understanding of Babesia diversity increasingly rely on genetic data obtained through molecular techniques.
View Article and Find Full Text PDFJ Neural Transm (Vienna)
January 2025
Section of Adult Neurology, Department of Internal Medicine, Chong Hua Hospital, Fuente, Cebu, Philippines.
Joubert Syndrome (JS) is a congenital cerebellar ataxia typically inherited in an autosomal recessive pattern, although rare X-linked inheritance can occur. It is characterized by hypotonia evolving into ataxia, global developmental delay, oculomotor apraxia, breathing dysregulation, and multiorgan involvement. To date, there are 40 causative genes implicated in JS, all of which encode proteins of the primary cilium.
View Article and Find Full Text PDFCurr Genet
January 2025
Department of Prosthodontics, King George's Medical University, Lucknow, 226003, India.
Dental plaque biofilms are the primary etiologic factor for various chronic oral infectious diseases. In recent years, dental plaque shows enormous potential to know about an individual microbiota. Various microbiome studies of oral cavity from different geographical locations reveals abundance of microbial species.
View Article and Find Full Text PDFGraefes Arch Clin Exp Ophthalmol
January 2025
Londrina State University, Avenida Robert Koch, 60, Londrina, CEP 86038-440, Paraná, Brazil.
Purpose: To detect choroidal markers of disease activity in eyes with chronic active unilateral pachychoroid disease spectrum (PDS) compared to the non-active contralateral eyes, based on multimodal imaging and particularly indocyanine green angiography (ICGA).
Methods: An observational retrospective case-control study. The study evaluated individuals diagnosed with chronic active unilateral PDS (group 1) by comparing the eyes with the non-active fellow eyes (group 2).
Sci Rep
January 2025
Institute for Anthropological Research, 10000, Zagreb, Croatia.
Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer learning (TL) leverage knowledge from a model trained on a rich dataset to enhance one trained on a sparse dataset, provided there are similarities in data distribution.
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