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Menstrual health and period poverty among young people who menstruate in the Barcelona metropolitan area (Spain): protocol of a mixed-methods study.

Laura Medina-PeruchaConstanza Jacques-AviñóCarme Valls-LlobetRosa Turbau-VallsDiana PinzónLola HernándezPaula Briales CansecoTomàs López-JiménezEnara Solana LizarzaJordina Munrós FeliuAnna Berenguera
Published in: BMJ open (2020)
This is a convergent mixed-methods study, which will combine a quantitative transversal study to identify the prevalence of period poverty among YPM (11-16 years old), and a qualitative study that will focus on exploring menstruation-related experiences of YPM and other groups (young people who do not menstruate (YNM); primary healthcare professionals; educators and policy-makers). The study will be conducted in the Barcelona metropolitan area between 2020 and 2021. Eighteen schools and 871 YPM will be recruited for the quantitative study. Sixty-five YPM will participate in the qualitative study. Forty-five YNM and 12 professionals will also be recruited to take part in the qualitative study. Socioeconomic and cultural diversity will be main vectors for recruitment, to ensure the findings are representative to the social and cultural context. Descriptive statistics will be performed for each variable to identify asymmetric distributions and differences among groups will be evaluated. Thematic analysis will be used for qualitative data analyses ETHICS AND DISSEMINATION: Several ethical issues have been considered, especially as this study includes the participation of underage participants. The study has received ethical approval by the IDIAPJGol Research Ethics Committee (19/178 P). Research findings will be disseminated to key audiences, such as YPM, YNM, parents/legal tutors, health professionals, educators, youth (and other relevant) organisations, general community members, stakeholders and policy-makers, and academia.
Keyphrases
  • mental health
  • healthcare
  • public health
  • randomized controlled trial
  • systematic review
  • machine learning
  • big data
  • young adults
  • risk assessment
  • artificial intelligence
  • high resolution
  • human health