Associations Between Race-based and Sex-based Discrimination, Health, and Functioning
Bibliographic record
Abstract
BACKGROUND: Only a few studies have examined race-based discrimination (RBD) and sex-based discrimination (SBD) in military samples and all are cross-sectional. OBJECTIVES: The current study examined associations between both RBD and SBD experienced during Marine recruit training and several health and functioning outcomes 11 years later in a racially/ethnically diverse sample of men and women. RESEARCH DESIGN: Linear multiple regression models were used to examine associations between sex, race/ethnicity, RBD and SBD, and later outcomes (physical health, self-esteem, and occupational/vocational functioning), accounting for baseline levels and covariates. SUBJECTS: Data were drawn from a larger longitudinal investigation of US Marine Corps recruits. The sample (N=471) was comprised of white men (34.6%), white women (37.6%), racial/ethnic minority men (12.7%), and racial/ethnic minority women (15.1%). MEASURES: Self-report measures of sex and race (T1), RBD and SBD (T2), social support (T2), mental health (T2), physical health (T2 and T5), self-esteem (T2 and T5), and occupational/vocational functioning (T5) were included. RESULTS: Over a decade later, experiences of RBD were negatively associated with physical health and self-esteem. Social support was the strongest predictor of occupational/vocational functioning. Effects of sex, SBD, and minority status were not significant in regressions after accounting for other variables. CONCLUSIONS: Health care providers can play a key role in tailoring care to the needs of these important subpopulations of veterans by assessing and acknowledging experiences of discrimination and remaining aware of the potential negative associations between discrimination and health and functioning above and beyond the contributions of sex and race/ethnicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".