Experiences of pregnant Venezuelan migrants/refugees in Brazil, Ecuador and Peru: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: It is estimated that since 2014, approximately 7.3 million Venezuelan migrants/refugees have left the country. Although both male and female migrants/refugees are vulnerable, female migrants/refugees are more likely to face discrimination, emotional, physical, and sexual violence. Currently there is a lack of literature that explores the experiences of pregnant Venezuelan migrants/refugees. Our aim is to better understand the experience of this vulnerable population to inform programming. METHODS: In the parent study, Spryng.io's sensemaking tool was used to gain insight into the gendered migration experiences of Venezuelan women/girls. A total of 9339 micronarratives were collected from 9116 unique participants in Peru, Ecuador and Brazil from January to April 2022. For the purpose of this analysis, two independent reviewers screened 817 micronarratives which were identified by the participant as being about someone who was pregnant, ultimately including 231 as part of the thematic analysis. This was an exploratory study and an open thematic analysis of the narratives was performed. RESULTS: The mean age and standard deviation of our population was 25.77 ± 6.73. The majority of women in the sample already had at least 1 child (62%), were married at the time of migration (53%) and identified as low socio-economic status (59%). The qualitative analysis revealed the following main themes among pregnant Venezuelan migrants/refugees: xenophobia in the forms of racial slurs and hostile treatment from health-care workers while accessing pregnancy care; sexual, physical, and verbal violence experienced during migration; lack of shelter, resources and financial support; and travelling with the hopes of a better future. CONCLUSION: Pregnant Venezuelan migrants/refugees are a vulnerable population that encounter complex gender-based and societal issues that are rarely sufficiently reported. The findings of this study can inform governments, non-governmental organizations, and international organizations to improve support systems for pregnant migrants/refugees. Based on the results of our study we recommend addressing xenophobia in health-care centres and the lack of shelter and food in host countries at various levels, creating support spaces for pregnant women who experience trauma or violence, and connecting women with reliable employment opportunities and maternal healthcare.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".