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Upscaling a behavioural intervention program for tobacco use cessation - A randomised controlled study.

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A train-the-trainer model successfully scaled an evidence-based tobacco intervention among school teachers in India. This approach effectively utilized existing resources to implement the program widely.

Keywords:
InterventionPolicySchoolsTeachersTobacco

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Area of Science:

  • Public Health
  • Health Education
  • Intervention Science

Background:

  • Tobacco use remains a significant public health concern, necessitating effective intervention strategies.
  • Scaling up evidence-based interventions is crucial for widespread impact but often faces resource limitations.

Purpose of the Study:

  • To evaluate the effectiveness of a train-the-trainer model for upscaling a tobacco intervention among school teachers in Bihar, India.
  • To assess the feasibility of implementing the intervention using existing resources within the school system.

Main Methods:

  • A randomized control trial was conducted across six blocks in three districts.
  • Intervention and control blocks were established, with cluster coordinators and headmasters in intervention blocks receiving training.
  • Post-intervention surveys assessed implementation fidelity and outcomes among teachers and headmasters.

Main Results:

  • High training attendance and teacher involvement were observed among headmasters in intervention schools.
  • Significantly higher odds of carrying out recommended activities were found in intervention schools compared to control schools.
  • Intervention schools spontaneously adopted additional activities like classroom integration and parent communication.

Conclusions:

  • The train-the-trainer model is a feasible and effective strategy for upscaling evidence-based health interventions.
  • Leveraging existing human resources and materials within educational systems can facilitate successful intervention scale-up.
  • This model offers a sustainable approach to public health program dissemination in resource-constrained settings.