Chinese university students’ help-seeking behaviors when faced with mental health challenges
Bibliographic record
Abstract
BACKGROUND: Mental illnesses and mental health challenges have become increasingly pervasive among Chinese university students. However, the utilization rate of mental health services is low among students. AIMS: We aimed to explore Chinese university students' help-seeking behaviors to understand how they deal with mental health challenges and use the results to inform the development of effective mental health promotion initiatives. METHODS: In this study, we conducted 13 focus group interviews with students in six universities in Jinan, China, including 91 (62%) female students and 56 (38%) male students. We drew on the Theory of Planned Behaviors to guide our thematic analysis to gain a contextual understanding of participants' accounts on help-seeking. RESULTS: Our results have depicted the help-seeking patterns of Chinese university students and show that there are four major behaviors which are self-reliance, seeking support from peers and families, seeking professional support, and accessing virtual mental health care. CONCLUSION: Results from this study can be used to inform the development of mental health literacy programming for students in universities that share similar contexts, and the study has also opened up a new space for using qualitative approaches to study mental health needs and access to care in diverse populations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".