The use of digitally enabled technology is considered a promising platform to prevent morbidity and enhance youth mental health as youth are growing up in the digital world and accessing the Internet at increasingly younger age. This scoping review will identify, describe and categorise the models, frameworks and strategies that have been used to study the implementation of digital mental health interventions targeted at youth aged 15–34 years.

We will conduct a scoping review following the Arksey-O’Malley five-stage scoping review method and the Scoping Review Methods Manual by the Joanna Briggs Institute. Implementation methods will be operationalised according to pre-established aims: (1) process models that describe or guide the implementation process; (2) evaluation frameworks evaluating or measuring the success of implementation; and (3) implementation strategies used in isolation or combination in implementation research and practice. Primary research studies in all languages will be identified in CINAHL, Cochrane Central Register of Controlled Trials, Embase, ERIC, Education Research Complete, MEDLINE and APA PsycINFO on 6 January 2025. Two reviewers will calibrate screening criteria and the data charting form and will independently screen records and abstract data. We will use the Evidence Standards Framework for Digital Health Technologies by the National Institute for Health and Care Excellence to classify digital interventions based on functions, and a pre-established working taxonomy to synthesise conceptually distinct implementation outcomes. Convergent integrated data synthesis will be performed.

Ethical approval is not applicable as this scoping review will be conducted only on data presented in the published literature. Findings will be published and directly infused into our multidisciplinary team of academic researchers, youth partners, health professionals and knowledge users (healthcare and non-governmental organisation decision makers) to co-design and pilot test a digital psychoeducational health intervention to engage, educate and empower youth to be informed stewards of their mental health.

This study aims to assess the feasibility of respondent-driven sampling (RDS) to recruit participants with recent abortion experiences in humanitarian contexts, and describe the composition of the study sample generated with this sampling method.

This was a three-phase mixed-methods community-engaged research study employing an exploratory and explanatory sequential approach. We conducted in-depth interviews, focus group discussions, an interviewer-administered questionnaire on abortion experiences and a health facility assessment.

Bidibidi Refugee Settlement, Uganda and Kakuma Refugee Camp, Kenya from November 2021 to December 2022.

Using RDS, we recruited 600 participants in Kakuma and 601 participants in Bidibidi with recent abortion experiences. In Kakuma, participants were primarily from Burundi, the Democratic Republic of the Congo and South Sudan; participants in Bidibidi were primarily from South Sudan. Most participants in both sites had completed at least some primary school and were not employed.

RDS recruitment dynamics: convergence and bottlenecks on key sociodemographic variables, recruitment and population homophily, reciprocity of social ties, success and experiences recruiting.

There were minor violations of RDS assumptions, particularly regarding assumptions of reciprocity of ties and seed composition independent of sample. In addition, there was a strong tendency of participants to recruit those from the same home country and living within the same camp zone. However, sample proportions for age, home country, marital status, zone of residence and student status reached equilibrium (stabilised) by around 500 participants at each site, and we were able to quickly attain the study sample size.

While the true representativeness of our sample remains unknown, RDS is a practical and effective recruitment method in humanitarian contexts for sensitive topics, particularly for research questions in which no data or sampling frames exist. However, attention to representativeness and community engagement is essential to optimising its application and ensuring success.