Suicidality among Immigrants: A Qualitative Profile of Studies on Experiences of Immigrants in Canada, United Kingdom and United States of America
Bibliographic record
Abstract
Introduction: Migration is a hallmark feature of globalization. Migration is a global phenomenon; thereby, mental health of individuals must also be considered from the viewpoint of broader indicators such as migration. Migration, mental health and suicidality are connected in a unique pattern in the globalized world. Purpose: The purpose of this study is to understand patterns of suicidality among immigrants by considering their experiences in Canada, United Kingdom and United States of America. Methodology: This study is a multicounty thematic review. Databases including Sociological Abstracts, CINAHIL and EBSCO host, Medline and EMBASE were searched to identify studies through keyword search. Braun and Clark (2019) thematic analysis technique was used to analyze the data. Results: A total of 5 major themes were extracted which also included sub-themes. There is evidence of prevalence of higher suicidality among immigrants. Suicidal behavior and specific countries of origin are considerable regarding suicidality. Key contributors to suicidality among immigrants in Canada, UK and the USA include language barriers, worrying about family back home, separation from family, assimilation and acculturation and homelessness. Social support and protective factors can be effectively used with understanding and realization of the issue. Conclusion: Immigrants in Canada, UK and the USA are more vulnerable to suicidality as compared to other segments of population. Mental health is the major culprit whereby social, economic and cultural factors make immigrants vulnerable to suicidality
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".