Mental Health Literacy in Emerging Adults in a University Setting: Distinctions Between Symptom Awareness and Appraisal
Bibliographic record
Abstract
Despite the high prevalence of mental health concerns in university populations, students are unlikely to seek formal help. The current study examined help-seeking behaviors among emerging adults in a university setting using a mental health literacy framework. Responses from 122 university undergraduates were examined. Students ranged in age from 17 to 25 years ( M = 20.67 years, SD = 2.03 years). Quantitative data were collected to determine students’ knowledge and use of campus services and to compare students’ awareness and appraisal of common mental health symptoms. Narrative data were collected to identify the key symptoms that are appraised to be “early warning signs” versus “early action signs,” and to identify barriers and facilitators to help-seeking. Mental health symptoms were more likely to be assessed as warning signs than signs warranting action. Lack of knowledge and stigma were barriers to help-seeking, while urging from family and friends, increased knowledge, and confidentiality were identified as facilitators to help-seeking. Emerging adults in a university setting tend to make distinctions between warning and action signs. Although demonstrating good awareness of the signs of declining mental health, students may respond reactively rather than proactively to symptoms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".