Understanding Loneliness in Younger People: Review of the Opportunities and Challenges for Loneliness Interventions
Bibliographic record
Abstract
Loneliness affects the quality of life of people all around the world. Loneliness is also shown to be directly associated with mental health issues and is often the cause of mental health problems. It is also shown to increase the risk of heart diseases and other physical illnesses. Loneliness is studied both from the social and medical sciences perspectives. There are also interventions on the basis of health informatics, information and communication technologies (ICTs), social media, and other technological solutions. In the literature, loneliness is studied from various angles and perspectives ranging from biological to socioeconomical and through anthropological understandings of technology. From the ICT and technological sides, there are multiple reviews studying the effectiveness of intervention strategies and solutions. However, there is a lack of a comprehensive review on loneliness that engulfs the psychological, social, and technological studies of loneliness. From the perspective of loneliness informatics (ie, the application of health informatics practices and tools), it is important to understand the psychological and biological basis of loneliness. When it comes to technological interventions to fight off loneliness, the majority of interventions focus on older people. While loneliness is highest among older people, theoretical and demographical studies of loneliness give a U-shaped distribution age-wise to loneliness; that is, younger people and older people are the demographics most affected by loneliness. But the strategies and interventions designed for older people cannot be directly applied to younger people. We present the dynamics of loneliness in younger people and also provide an overview of the technological interventions for loneliness in younger people. This paper presents an approach wherein the studies carried out from the perspectives of digital health and informatics are discussed in detail. A comprehensive overview of the understanding of loneliness and the study of the overall field of tools and strategies of loneliness informatics was carried out. The need to study loneliness in younger people is addressed and particular digital solutions and interventions developed for younger people are presented. This paper can be used to overcome the challenges of technological gaps in the studies and strategies developed for loneliness. The findings of this study show that the majority of interventions and reviews are focused on older people, with ICT-based and social media-based interventions showing promise for countering the effects of loneliness. There are new technologies, such as conversational agents and robots, which are tailored to the particular needs of younger people. This literature review suggests that the digital solutions developed to overcome loneliness can benefit people, and younger people in particular, more if they are made interactive in order to retain users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".