University Students’ Self-Rated Health and Use of Health Services: A Secondary Analysis
Bibliographic record
Abstract
BACKGROUND: University students belonging to various ethnic groups have specific health needs that influence their self-rated health and health service use. PURPOSE: To examine which determinants of health serve as key predictors of self-rated health and health service use in a sample of ethnically diverse undergraduate students. METHODS: (N = 10,512). Logistic regression was used to explore the predictors of self-rated health and use of university-based health services according to ethnicity. RESULTS: Social support (Caucasian: odds ratio (OR) = 1.018; 95% confidence interval (CI) [1.008, 1.028]; African: OR = 1.890; 95% CI [1.022, 1.160]; Other: OR = 1.096; 95% CI [1.023, 1.175]), and depression risk (Caucasian: OR = .899; 95% CI [.844, .914]; Indigenous: OR = .904; 95% CI [.844, .969]; Asian: OR = .894; 95% CI [.839, .953]; Multiracial: OR = .892; 95% CI [.812, .980]) were the most frequent predictors of self-rated health across the different ethnic groups; while year of study (Caucasian: OR = 1.855; 95% CI [1.764, 1.952]; African: OR = 2.979; 95% CI [2.068, 4.291]; Indigenous OR = 1.828; 95% CI [1.371, 2.436]; Asian: OR = 1.457; 95% CI [1.818, 1.797]; Middle Eastern: OR = 1.602; 95% CI [1.088, 2.359]; Other: OR = 1.485; 95% CI [1.093, 2.018]; Multiracial: OR = 2.064; 95% CI [1.533, 2.778]) was found to be the most significant predictor of health service use. CONCLUSION: Findings from this research shed light on the various factors that impact university students belonging to different ethnic groups, their health, and their access to healthcare that addresses their distinct health needs. Nurses can advocate for the development of health promotion and illness prevention strategies that target the needs of the diverse student population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".