Psychological health and discrimination experience among graduate students: findings from the Stress Coping Obstruction Prevention & Education (SCOPE) Study
Bibliographic record
Abstract
Purpose – African-Americans historically report greater exposure to discrimination and also experience unfavorable outcomes associated with physical health, poverty concentration, residential segregation, and poorer education. The effects of discrimination are particularly harmful on mental health as discriminatory experiences contribute significantly to diminished mental health status and psychological distress. African-Americans pursuing graduate education may experience additional stressors, increasing the risk for poorer mental health. The purpose of this paper is to examine the association of psychological health and discrimination experiences among black and white graduate students at a southeastern university. Design/methodology/approach – Participants were 505 graduate students at a predominantly white southeastern institution. Researchers collected data via self-administered online and paper questionnaires during the spring 2010 semester. Graduate students were asked questions pertaining to individual demographics, discrimination, and psychosocial concerns. Findings – Approximately 15 percent of the graduate students reported psychological distress. Additionally, black graduate students reported significantly higher levels of day-to-day and lifetime discrimination when compared to white graduate students. In addition to the proportions of psychological distress differing by race, African-American graduate students reported better psychological well-being when exposed to both day-to-day and lifetime discrimination than whites with similar exposure. Practical implications – Resilience factors and coping strategies should be examined further among African-American graduate students for greater understanding. Moreover, it is important to develop applications to improve mental health outcomes for all graduate students. Originality/value – This is one of the few studies to focus on the mental health and discrimination experiences among a graduate student population. The sample is drawn from the southeastern USA where there are long vestiges of discrimination and a sizable sampling of African-Americans who live in the USA.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".