The mediating effect of social support on the relationship between the impact of experienced stigma and mental health.
Bibliographic record
Abstract
The impact of stigma and discrimination against persons with mental illness is well documented. Less well researched are the interpersonal and intrapersonal mechanisms that mediate how acts of discrimination impact persons with mental illness, specifically social support. Past research has focused on the buffering, or moderating impact of perceived social support. We hypothesize that perceived social support is a psychological process, changed by interactions with the outside world, including stressful interactions. In this study, we explore perceived social support as a mediator between the impact of experienced discrimination and mental health. We also test the moderating hypotheses as a way to determine if past research on the role of perceived social support is a better model than the mediating model. We used data from a subset of the Canadian Community Health Survey–Mental Health. We tested the mediating role of perceived social support using the bootstrapped estimate of the 95% confidence interval of the indirect effect. We also tested the buffering hypothesis of perceived social support, using the product of the impact of decimation measure and perceived social support measure. The results suggest that perceived social support does mediate the relationship between the impact of experienced discrimination and mental health. The buffering hypothesis did not hold. Results suggest a new way to model the relationship of perceived social support, stigma, and mental health. Further, the results provide insights into the importance of intervening at the point of discrimination.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".