A Review of Advances in Social Sciences and their Application for Research in Suicidal Behavior
Bibliographic record
Abstract
Suicidal behavior and its prevention constitute a major public health issue, and the moderating effect of sociodemographic factors has been studied for more than a century. In the last years it has become evident that the relationship between social factors and suicidal behavior is complex and highly dependent on the context. For instance, minorities suffering marginalization, such as the Inuit in Canada or the aborigines in Australia, present high rates of suicide. However, other minorities, such as immigrants arriving to tightened communities, can be protected from suicide compared to the social majority. Other contradictory effects have been reported concerning income per capita and the evolution of the economy. Unfortunately, the interplay of social factors in suicidal behavior and the social consequences of suicide attempts are rarely represented in theoretical models of suicidal behavior, despite their importance to adapt suicide prevention policies to social groups at risk. In this presentation, recent advances and new and integrative avenues for future research in the social aspects of suicidal behavior will be summarized. Disclosure of interest The author declares that he has no competing interest.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".