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This research aims at explaining the phenomenon of the “black children” (heihaizi), a very little-known generation who lived with concealment under the one-child policy in China. The one-child policy was officially introduced to nationwide at the end of 1979 by permitting per couple to have one child only, later modified to a second child allowed if the first was a girl in rural China in 1984. It was officially replaced by a nation-wide two-child policy and most existing research focused on the parents’ sufferings and policy changes. The term “black children” has been mainly used to describe their absence from their family hukou registration and education. However, this research aims at expanding the meaning of being “black” to explain the children who were concealed more than at the level of family formal registration, but also physical freedom and emotional bond. What we do not yet know are the details of their lived experiences from a day-to-day base: where did they live? How were they raised up? Who were involved? Who benefited from it and who did not? In this way, this research challenges the existing scholarship on the one-child policy and repositions the “black children” as primary victims, and reveals the family as a key figure in co-producing their diminished status with the support of state power. It is very important to understand these children’s loss of citizenship and human freedom from the inside of the family because they were concealed in so many ways away from public view and interventions. This research focuses on illustrating how their lack of access to continued, stabilized, and reciprocally recognized family interactions framed their very idea of self-worth and identity.

This paper aims to understand the maternal health vulnerabilities of migrant women in slums and explore their challenges during and after childbirth.

The study used a qualitative approach, including in-depth interviews through purposive and snowball sampling techniques. Thematic analysis was used for analysing data. The consolidated criteria for reporting qualitative studies (COREQ)-32 items were followed for reporting this study.

The study found that migrant women were highly susceptible to adverse birthing outcomes due to risks involved in birthing, lack of care and hygiene, lack of skilled care in dealing with complicated pregnancies and exposure to domestic and obstetric violence.

The study intends to highlight the narratives of female migrants’ birthing and maternal health challenges. The entire process of childbirth in slums with consequences can result in maternal and infant morbidities and mortalities.

Hikikomori is a multidimensional condition, characterized by voluntarily social withdrawal, impacting the relational dimension of life. The current study aims to examine secondary school teachers' beliefs, knowledge and needs on hikikomori and students' social withdrawal.

A qualitative method consisting of a semi-structured interview is adopted with 22 Italian secondary school teachers. The interview questions are focused on the beliefs, the profile, the conditions and the role of the school for adolescents socially retired.

Data are analysed using content analysis based on the grounded theory framework. Forty-nine codes emerged from the inductive analysis, which were sorted into the following categories: (1) Characteristics of hikikomori and social withdrawal; (2) Origin, causes and consequences; (3) Sources of information; (4) Socio-relational modalities; (5) Teachers' needs and role of the school. Teachers are aware of the educational issues and risks related to hikikomori and claim for more institutional support. Teachers reconsider the way of working in class for preventing the risk of self-isolation, supporting the development of social and emotional skills, and encouraging collaboration and positive exchanges among students. Participants mention a personalized student-centred method where families and external agencies support the school system.

Although several clinical and psychological interventions have been developed for treating the hikikomori’s self-isolation and concomitant mental disorders, few plans have been implemented for reducing the risk of adolescents' social withdrawal. Preventing hikikomori is crucial as well as to investigate the role and the needs of school teachers, and the current study has tried to explore these.

As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers’ tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences’ engagement.

Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement.

In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers’ tweets, the topics of “Child protection” and “COVID-19 situation” are positively predicting audiences’ engagement. For anti-vaxxers, the topics of “Supporting Trump,” “Injured children,” “COVID-19 situation,” “Media propaganda” and “Community building” are more appealing to audiences.

This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186