Antisemitism on Campus in the Wake of October 7: Examining Stress, Coping, and Depressive Symptoms Among Jewish Students
Bibliographic record
Abstract
The Hamas-led terrorist attacks in Israel on October 7, 2023, were an inflection point that spurred a global rise in antisemitism. College and university campuses were particularly affected. Given the adverse impacts of prejudice and discrimination for mental health and the dearth of research on psychosocial effects of antisemitism, examining stress, coping, and mental health among Jewish students within this context is crucial. In this study, we used longitudinal data and multilevel modelling in the months after the events of October 7 to examine within- and between-person effects of antisemitism-related stress, stress associated with the Israel-Palestine conflict, and approach and avoidance coping on depressive symptoms among a sample of 253 Jewish college and university students. Within-person results indicated that increases from one's usual level of antisemitism-related stress and stress attributable to the Israel-Palestine conflict were independently associated with heightened depressive symptoms. Increased use of avoidance coping was also associated with heightened depressive symptoms, whereas the inverse was true for approach coping which had a protective effect. Similar effects were observed at the between-person level. Results from the present work draw attention to both the potentially detrimental effects of stress associated with antisemitism, but also highlight coping as a target for intervention to potentially combat such effects. The findings also point to a path forward where campuses can remain open areas of inquiry by fostering resilience at both the community and individual level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".