Review of Loneliness Research Published within 2024-2025: A Bibliometric Analysis and its Implications in India
Bibliographic record
Abstract
This study utilizes Bibliometric analysis, extracted from the Scopus database and employs VOSviewer, to explore the global landscape of recent loneliness research from 2024 to 2025.Extracting 396 publications through keyword searches "Loneliness" in title and abstract, the analysis reveals a significant concentration of research output from Western nations, including the USA, UK, and Canada, alongside notable contributions from China and Australia.This highlights the perceived public health importance of loneliness in these regions.However, absence of India among the top 11 publishing countries is observed, despite its vast and diverse population.This absence suggests a potential research gap in addressing loneliness within the Indian context.The co-occurrence of keywords elucidates dominant thematic areas, with clusters emphasizing the mental health implications of loneliness, particularly among vulnerable groups like older adults.The pandemic's impact on adolescent mental health is also prominently featured, with clusters like "adolescents," "Covid-19," and "mental health."These global research trends raise critical questions about their applicability and scope for research in India.The study highlights the necessity to examine the unique challenges faced by India's population with regard to loneliness, considering factors like evolving family structures, work life imbalance and limited social support.Similarly, the impact of Covid-19 on the mental health of the population India must be understood within the context of existing social inequalities and resource limitations.The findings propose for a deeper investigation into the dimensions of loneliness with special reference to India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.173 | 0.237 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".