Attitudes toward Mental Illness among Minority Freshmen in China
Bibliographic record
Abstract
To explore the status and influencing factors of attitudes toward mental illness among minority freshmen and provide references for mental health education and service assistance in ethnic colleges, a questionnaire survey was conducted among 581 minority freshmen by using Mental Illness Emotional Scale, Stereotype Scale and Social Distance Scale from autumn of 2018 to next spring. The scores of minority freshmen on these scales were significantly higher than the theoretical median, which demonstrated that they did have negative attitudes. There were the significant main effect of nation on the anger and sympathy dimensions of Mental Illness Emotional Scale while the same results were found in the main effect of gender on the danger and suicidal behavior dimensions of Mental Illness Stereotype Scale and the main effect of nation on the dimension of dependence consciousness. Whether relatives or friends are mental illness patients had significant effect on the suicidal behavior dimension of Mental Illness Stereotypes Scale. Overall, attitudes toward mental illness were negative and some mental illness stigma phenomenon were found among minority freshmen; ethnic was an important factor affecting the attitudes of minority freshmen toward mental illness, and the inter-group contact with patients who suffer mental illness in life had a slight impact on the attitudes toward mental illness.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".