Culture and suicide : perspectives of first-generation Korean-Canadian immigrants
Bibliographic record
Abstract
Background: Suicide is a serious health concern worldwide. In 2007, almost 4,000 Canadians took their own lives and among older and middle-age groups, suicide is one of the leading causes of death for both men and women. Given the far-reaching impact on families and societies, suicide has been widely studied; yet, accounts about the connections between suicide and culture in the context of immigrant populations are still poorly understood. Objective: To better understand the connections between suicide and culture, and to provide a foundation on which to build targeted culturally-sensitive suicide prevention programs, this research used qualitative research method to describe the perception and experiences of suicide of fifteen Korean-Canadian immigrants. Results: Three inductively derived themes were identified to detail the study findings: 1) perceptions of and attitudes toward suicide among Korean-Canadian immigrants; 2) narratives around the causes and triggers of their suicidal thoughts and behaviours; and 3) manifestations of and strategies to manage their suicidal thoughts and behaviours. Within these three themes, there are a total of nine sub-themes which are intricately connected. Discussion: While recognising and embodying stigma around suicide, participants understood the hopelessness and despair that could drive immigrants toward suicide. Causes and triggers for suicidal thoughts most often emerged from academic pressures, estranged family, and dis-identities – all of which were intricately connected to participants’ immigration experiences. Noteworthy were deeply embedded Confucian values, which could exert an array of influences on Korean-Canadians. In addition, extensively discussed were dis-identity experiences whereby a sense of self and as well as collectivist familial bonds were challenged, and suicidal ideation could flow toward and/or from these changes. Many participants were unaware of mental health services and programs amid being challenged by language barriers when they did access mental health services. While, it is critical for healthcare providers to understand immigrant patients’ cultural background to fully assess their risk for suicide, also urgently needed are targeted efforts to raise public awareness about suicide and educate immigrants about professional and self-help options to manage their mental health and well-being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".