ABSTRACT
Introduction: Early identification of Autistic children is an important precursor to diagnosis, and access to supports and services. Here we describe the training of the maternal and child health (MCH) workforce in the state of Victoria, Australia in the early identification of infants and toddlers with a high likelihood of autism. Methods: In 2019, 1,428 MCH nurses completed early autism training held at venues across the state, with an additional 82 nurses completing online-only training. A training needs analysis enabled the research team to determine the workforce's current skill and knowledge levels, and to identify knowledge gaps, training needs and workplace barriers. The professional development program, known as Monitoring of Social Attention, Interaction, and Communication (MoSAIC), comprised: online pre-workshop modules; a face-to-face instructor-led workshop, which included the use of the Social Attention and Communication-Revised (SACS-R) tool; and online post-workshop modules, which included a recording of a face-to-face workshop with all accompanying resources. This was the first time that the MCH workforce received this training package. Attendees were asked to complete a training satisfaction survey immediately following the face-to-face instructor-led workshop and a follow-up survey regarding their autism knowledge and SACS-R implementation 4-6 weeks after the workshop. Results: Over 90% (n = 325) of MCH nurses who completed the training satisfaction survey agreed or strongly agreed with statements that the training was clear and of high quality. Most nurses also reported that the training was well-presented and that they would recommend it to a colleague. In the 6 months following the training, a total of 82,581 SACS-R assessments were conducted by the MCH workforce, reflecting that MCH nurses had successfully integrated SACS-R assessments into their work practice after receiving the early autism identification training. Discussion: This study demonstrated that training on the early identification of autism can be successfully designed, customized, and delivered to a large primary healthcare workforce for universal developmental surveillance of autism.
ABSTRACT
Introduction: Early identification of children with a high likelihood of autism can lead to referral for diagnostic services and access to early supports, resulting in improved outcomes for children and families. Maternal and Child Health Nurses (MCHNs) in Victoria, Australia, are well-placed to monitor infants and toddlers for signs of autism, given children and caregivers attend free, regular, well-baby consultations from birth through to school age. This study aimed to identify the impact of personal and workplace factors on MCHNs' competencies of autism knowledge, self-efficacy in identifying autistic infants and toddlers, and confidence in speaking to parents/caregivers about autism. Additionally, the study sought to identify which personal and workplace factors might predict increased competency in these areas. Methods: After identifying training needs and current competency levels via a training needs analysis (TNA), 1,428 MCHNs received training on the early signs of autism and in the use of the Social Attention and Communication Surveillance-Revised (SACS-R) tool for early autism identification; the training program was known as Monitoring of Social Attention, Interaction, and Communication (MoSAIC). Results: Previous MCHN autism training and knowledge of autism community resources significantly contributed to increased MCHN self-efficacy in identifying autistic infants and toddlers, while knowledge of community resources was the best predictor of confidence in speaking with parents/caregivers about autism. Perceived self-efficacy and confidence in speaking with parents/caregivers about autism significantly increased following the MoSAIC autism training. Discussion: Targeted autism training for primary health practitioners is an important first step for early autism identification and initiating conversations with parents/caregivers.
