Reducing the Stigma of Depression Among Asian Students
Bibliographic record
Abstract
In North America, Asians reliably report higher levels of stigma toward people with depression than do Europeans. Possible methods of reducing this discrepancy have rarely been explored. Asian undergraduate students ( n = 132) were presented with one of four antistigma videos with two actresses: one portraying a student with depression and the other a professor. The videos used the concept of social proof, presenting either positive or negative descriptive norms, to effect change in stigma, measured by social distance. It was hypothesized that the positive descriptive norms intervention would show significantly greater positive change in social distance compared with the negative descriptive norms intervention. All videos were effective in reducing preferred social distance toward people with depression relative to the control condition. The effectiveness of the positive descriptive norm video was mediated through descriptive norms and self-efficacy. The effectiveness of the negative descriptive norm video was mediated through injunctive norms and perceived value of support. The findings can help guide interventions that aim to encourage social engagement with people with depression among Asian student populations. Manipulating social norms and increasing self-efficacy may be especially effective.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".