What links can be made from narratives of migration and self-perceived health? A qualitative study with Haitian migrants settling in Quebec after the 2010 Haiti earthquake
Bibliographic record
Abstract
In January 2010, Haiti was hit by a terrible earthquake that pushed thousands of people to migrate. Many of them chose to settle in Quebec, Canada. Years after the earthquake, many Haitians continue to migrate to the Quebec province. Several studies however have shown that this population's socioeconomic status is lower than the provincial average. Given the potential multiple stressors that affect Haitian migrants in Quebec, there are concerns about their health status. Located at the intersection of international migration studies and global public health, this paper offers an in-depth qualitative investigation of Haitian migrants' representations of both their situation and self-perceived health in Montreal, Quebec. Our perspective on migrant health was inspired by the World Health Organisation's framework on the social determinants of health and recent studies in the field of migrant health. We collected and analysed qualitative data from 23 key informants (i.e., 12 women and 11 men, aged 21-76 years old) from diverse socioeconomic backgrounds. The analysis of the data from these people's narratives revealed the importance of structural determinants such as social position, and intermediate determinants such as living and working conditions. Our analysis also highlighted several interrelations between those determinants. Specifically, participants reported coping with issues related to migration status, income, occupation, language, challenging living and working conditions, and chronic stress. This study also shows that racism and social support each relate to both the structural and intermediate levels of the social determinants of health. The importance of social support brought by relatives, friends, as well as community-based organisations and religious practice, was underscored. Our findings were coherent with available literature looking at the determinants of health of racialized and migrant minorities in other high-income regions of the world. Our conclusive remarks featured reflections on three cross-cutting issues and their practical implications for policy and practices.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".