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Cannabis as a recreational drug is prohibited in Nigeria. Consequently, the open use of cannabis attracts both formal and informal sanctions. As such, there is much stigma on users' faces across social spaces. This has led to innovations in drug use. Recently, non-medical use of tramadol has been rising across each of the gender categories. This study aims to understand (1) tramadol use prompts, (2) the sudden surge in gendered recreational use of tramadol and (3) the gendered challenges of recreational tramadol use among Nigerian university students.

Data were collected between December 2021 and October 2022. The researcher conducted 20 semi-structured interviews, with participants chosen purposefully from acquaintances and others selected through chain referral. The selection criteria included knowledge of a particular tramadol tablet and willingness to participate. This study got ethical approval from the Anambra State Ministry of Health (Ref: MH/AWK/M/321/354) and oral consent was obtained before the interviews. The participants were assured of confidentiality. The interviews were conducted in English (the formal Nigerian language) and lasted between 30 and 65 min. The data collected were transcribed and coded manually, and themes generated.

Findings suggest that peer pressures accounted for entry-level drug use. However, cannabis-related stigma is attributed to the surge in tramadol use across gender categories. Furthermore, academic pressure and sexuality are major reasons for tramadol use. The challenges associated with tramadol use include headaches and addiction.

This study, to the best of the author’s knowledge, presents alternative data on the surge in tramadol use among Nigerian university students.

Cannabis use continues to increase worldwide, and a number of nation states are changing their cannabis policies. Policy changes require research into key populations, namely, people who use cannabis. This study aims to examine sociodemographic differences of young Finns who reported using cannabis mainly for self-medication versus mainly recreationally, as well as their reported effects of cannabis use.

The data come from an anonymous online survey (N = 247, 70.0% males, 25.9% females, 4.1% other) that was analysed using multiple logistic regression. The authors focused on whether various demographic indicators differed between those who reportedly used cannabis mainly for recreational purposes and mainly for self-medicinal purposes. The authors also qualitatively examined the respondents’ experienced effects of cannabis, both desired and undesired.

Being older and female, living in a smaller city and earlier age of initiation of cannabis use were statistically significant in predicting the medicinal use of cannabis. The majority of recreational effects were related to themes such as relaxation and pleasure, but many participants also reported desired medical effects. Similarly, many participants reported several undesired effects.

Understanding especially young people’s motivations to use cannabis, which include using it for various medical effects, can improve the design of harm reduction and treatment programmes as well as enhance the well-being of people who use cannabis.

This study gives a nuanced account of sociodemographic factors and motivations of young people who use cannabis in Finland as well as the reported effects it has on them, which complements data from national drug surveys.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

Internationally, rates of suicide and lifetime self-harm are higher in prisoners compared to the general population. This study aims to identify specific characteristics of self-harming behaviour and to establish a profile of prisoners who engage in self-harm.

Data from the Self-Harm Assessment and Data Analysis Project (SADA) on self-harm episodes in prisons in the Republic of Ireland during 2017–2019 was used. Annual rates per 1,000 were calculated by age and gender.

The rate of self-harm between 2017 and 2019 was 31 per 1,000 prisoners for men and six times higher at 184 per 1,000 prisoners for women. The rate of self-harm was twice as high among prisoners on remand than sentenced prisoners (60.5 versus 31.3 per 1,000). The highest rates of self-harm among sentenced prisoners were observed among 18–29-year-old men (45 per 1,000) and women (125 per 1,000). The rate of self-harm was higher among women prisoners in all age groups. Contributory factors associated with self-harm were mainly related to mental health but also linked to a prisoner’s environment and relationships.

There is a need to ensure access to timely and suitable mental health services, including both appropriate referral and provision of evidence-based mental health interventions to address the needs of these cohorts.

To the best of the authors’ knowledge, this is the first national study to systematically examine incidence and patterns of self-harm among the prison population in Ireland. The recording of severity/intent of each episode is novel when assessing self-harm among the prison population.