Bibliometric Analysis of Psychological Distress Among Higher Learning Students in Africa
Bibliographic record
Abstract
This bibliometric study explores research trends, collaborative networks, and thematic focus areas within the field of psychological distress among higher education students in Africa. It offers a comprehensive overview of recent scholarly contributions, identifying key researchers, institutions, and publication patterns. A total of 877 empirical studies were retrieved from the Dimensions database using predefined search criteria. After a rigorous screening and eligibility assessment, 264 studies met all inclusion criteria and were included in the final analysis. Using VOSviewer 1.6.20 software, the study conducted network analyses and generated data visualizations to map research collaborations and thematic developments. The University of Cape Town and Addis Ababa University emerged as leading institutions in publishing research on psychological distress in Africa. Notably, countries such as South Africa, Ethiopia, Canada, Ghana, Kenya, and Uganda demonstrated high levels of international research collaboration in this domain. Keyword analysis revealed that the research is strongly tied to broader issues concerning societal, health, and psychological well-being. Thematic analysis identified several core research clusters, including the epidemiology of psychological distress, the impact of the COVID-19 pandemic, and mental health challenges among university students. Citation analysis further highlighted the most influential authors, institutions, and publication sources, offering deeper insights into the field’s academic landscape. This study provides critical insights into the evolution of research on psychological distress among higher education students in Africa, emphasizing key trends, collaborative patterns, and thematic developments that can inform future research and policy initiatives.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.157 | 0.223 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".