Bibliographic record
Abstract
The social and cultural dimensions of risk and protective factors for suicide within and between cultural, racial, and ethnic groups are poorly understood. We agree with Thompson that studies of groups, such as African Americans, that appear to have unique risk patterns for suicide could potentially inform our understanding not only of what renders some populations at higher risk of suicide, but also of the lesser studied role of protective factors. As we suggested in our article, suicide prevention has been slow to evolve, in large part owing to the limitations of using high-risk, primarily clinical approaches to prevention. We have called for integrating high-risk strategies (be they clinical or otherwise) with population-based approaches, which theoretically should benefit both entire cultural/ethnic/racial groups and individuals within the culture. Thompson’s comments suggest incorporating an additional perspective. There have been clear historic challenges in anthropology of connecting culture to individuals, resulting in a paucity of both theoretical perspectives and research methods. Both are necessary, neither one alone sufficient, were one to apply a “biopsychosocial”1 model to the study of suicide prevention. We have the theoretical and methodological frameworks to apply population-based approaches to suicide prevention. It is unclear whether we possess the same sophistication when it comes to studying the social and cultural dimensions of risk and protective factors for suicide. For many studies of suicide in men, samples have been drawn from predominantly White youth and elders, limiting the predictive power of such studies for girls and women and other age, racial, or ethnic groups. This limitation is gradually being recognized and addressed.2–4 Enriching our samples to include diverse groups is necessary, but this will demand that we redefine our measures of self-efficacy (bicultural self-efficacy), acculturation, and ethnic identity.5 These are complicated constructs that may not intuitively reflect accurate appraisals of the role of risk and protective factors for suicide or other deleterious outcomes. For example, Wong6 found that among Canadian Chinese adolescents, high acculturation was associated with a greater risk of delinquency. If accepting the attitudes and behaviors of the dominant culture is a potential risk factor, this will have direct implications for prevention of suicide and other violent behaviors. Engaging diverse groups in research studies will be insufficient, in and of itself, to elucidate the role that culture/ethnicity/race has in preventing suicide and other deleterious outcomes that may precede the taking of one’s own life. Such elucidation will require that we integrate rigorous measures of cultural perceptions of social belonging with an understanding of what the ramifications of belonging, or not belonging, may be.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.390 | 0.179 |
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".