Communal Riots and Its Psychological Impact: A Systematic Review Study in Indian Context
Bibliographic record
Abstract
Seema Irshad Department of Clinical Neurosciences, College of Medicine, King Faisal University, Al-Ahsa, Kingdom of Saudi ArabiaCorrespondence: Seema Irshad, Division of Psychology, Department of Clinical Neurosciences, College of Medicine, King Faisal University, Al-Ahsa, 31982, Kingdom of Saudi Arabia, Tel +966-13-589-7883 ; +966-538010765, Email sirshad@kfu.edu.sa; seemairshad81@gmail.comObjective: In the Indian setting, communal riots characterized by intergroup violence have been common and have had an impact on the lives of both people and communities. The goal of this systematic review is to investigate the psychological effects of Indian communal riots in depth. Through a comprehensive review of the literature, the study aims to identify recurring themes, patterns, and variances in the psychological effects encountered by victims of community violence.Methods: The researcher searched Science Direct, PubMed, Web of Science, Google Scholar, and PsycINFO for research published between the year 2000 and the year 2022 in each of these databases. The Newcastle–Ottawa Scale was employed to evaluate the study’s quality.Results: After identifying 1189 publications in all, 195 of them were chosen for full-text examination, and 41 research were ultimately included. Twenty studies examined depression and mental health with a prevalence rate of 49%. Five studies (12%) examined post-traumatic stress disorder. In places devastated by riots, the prevalence of post-traumatic stress disorder varied from 4% to 41%. Other studies investigated anxiety, alcohol abuse, and homelessness. However, two studies revealed that group activities could lower depression and suicide rates, perhaps as a result of increased social cohesiveness and group catharsis among subpopulations.Conclusion: The researcher investigated the connection between collective activities and mental health in this systematic review, providing strong evidence that riots, protests, and other collective actions—even peaceful ones—can harm mental health outcomes. Thus, healthcare providers must be aware of the psychological and emotional effects of riots, revolutions, and demonstrations. It is essential to do more study on this newly identified sociopolitical driver of mental health.Keywords: communal riots, psychological well-being, systematic review
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".