Bibliographic record
Abstract
Abstract This international volume offers new perspectives on social and psychological aspects of the complex dynamic of depression. The 21 contributors from 13 countries—Australia, Canada, Finland, Germany, Haiti, India, Israel, Nepal, Poland, Portugal, Puerto Rico, Scotland, and the United States—represent contexts with very different histories, political and economic structures, and gender role disparities. Authors use Silencing the Self theory, which details the negative psychological effects when individuals silence themselves in close relationships and the importance of the social context in precipitating depression. This book breaks new ground by demonstrating that the linkage of depressive symptoms with self-silencing occurs across a range of cultures. Chapters offer evidence regarding why women’s depression is more widespread than men’s and why the treatment of depression lies in understanding that a person’s individual psychology is inextricably related to the social world and to close relationships. Several chapters describe the transformative possibilities of community-driven movements for disadvantaged women that support healing through a recovery of voice, and describe the need for systemic and structural changes to counter violations of human rights as a means of reducing women’s risk of depression. Chapters also discuss health-related aspects of self-silencing, including eating disorders, treatment for cardiovascular disease, HIV/AIDS prevention and treatment, and self-care for women in cancer treatment. Bringing the work of these researchers together in one collection furthers international dialogue about critical social factors that affect the rising rates of depression around the globe.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".