A pilot study of adolescent health literacy research in Melbourne: Implementation and reflections

Abstract

Issues addressed While adolescent health literacy research has gained momentum, there is little evidence regarding its implementation and data collection in school settings. This study explored the feasibility of collecting health literacy data from Australian secondary schools and piloted three health literacy instruments. Methods A cross‐sectional study was designed to recruit four government secondary schools in Melbourne. Active, opt‐in consent was obtained from parents and students in Years 7‐9, and an online survey was conducted. Three health literacy instruments were used: the 8‐item Health Literacy Assessment Tool (HLAT‐8), the Newest Vital Sign (NVS), and the 47‐item Health Literacy Survey (HLS‐47). Results A total of 120 students (age 12‐15 years) were finally recruited from one school, whereas the other three schools declined due to busy educational commitment or no interest in research. Learnings and reflections on data collection included: a shared perspective of health literacy evaluation between school and researchers; the feasibility of online data collection; and the possibility of obtaining passive, opt‐out consent. About one‐quarter (23.7%‐32.2%) of students were likely to have poor health literacy. Conclusions Although the recruitment was challenging, this pilot study indicates the feasibility of large‐scale online health literacy survey in future school‐based research. So what? Measuring and monitoring adolescent health literacy is essential to achieve the aim of the Australian Curriculum of Health and Physical Education. More implementation research is needed with representative samples to validate health literacy instruments and examine the impact of health literacy on health promotion outcomes in Australian adolescents.

Factors related to health literacy among Brazilian adolescents: cross-sectional study

“…The survey with 650 Chinese adolescents found an average of 26.3 HL points (±5.89) (6) ; Meanwhile, in Italy, research with 806 university students found an average www.scielo.br/reeusp Rev Esc Enferm USP • 2024;58:e20230310 of 27.4 points (±4.4) (7) ;among 746 medical students in China, the average was 25.6 points (±4.5) (8) . The highest HL mean was reported in Australia in a survey of 120 adolescents with a mean of 28.25 (±6.0) (21) . The research carried out in Belo Horizonte, Brazil (12) , with 384 adolescents, using a Pediatric Quality of Life Questionnaire, indicated an adequate level of HL and reported that the best scores in the social and school domains were associated with the best HL scores.…”

“…The survey with 650 Chinese adolescents found an average of 26.3 HL points (±5.89) ( 6 ) ; Meanwhile, in Italy, research with 806 university students found an average of 27.4 points (±4.4) ( 7 ) ;among 746 medical students in China, the average was 25.6 points (±4.5) ( 8 ) . The highest HL mean was reported in Australia in a survey of 120 adolescents with a mean of 28.25 (±6.0) ( 21 ) .…”

Fatores relacionados ao letramento em saúde de adolescentes brasileiros: estudo transversal

“…A pesquisa com 650 adolescentes chineses encontrou uma média de 26,3 pontos de LS (±5,89) (6) ; enquanto isso, na Itália, a investigação com 806 universitários encontrou uma média de 27,4 pontos (±4,4) (7) ; entre 746 estudantes de medicina na China, a média foi de 25,6 pontos (±4,5) (8) . A maior média do LS foi relatada na Austrália em uma pesquisa com 120 adolescentes com média de 28,25 (±6,0) (21) .…”

Comparison of Health Literacy Assessment Tools among Beijing School-Aged Children

“…It remains unclear how skills-based health literacy instruments perform in Chinese children. In addition, little is known about the comparison of health literacy assessment tools in a single study and how the variation in measurement impacts the quantification of inequities in health outcomes [ 26 , 43 ]. Comparing the strengths and weaknesses of different health literacy instruments in measuring health inequities can inform researchers to better understand the underlying construct of each measure and how they perform unique roles in health disparities research.…”