The association between perceived psychosocial support and resilience among Venezuelan migrant women: A secondary analysis of cross-sectional data from 2022
Bibliographic record
Abstract
Migrants experience profound threats to their mental health, with women facing additional vulnerabilities such as sexual exploitation and trafficking. Resilience protects against the impacts of these threats through mental, emotional, and behavioural adaptations. A central component of resilience is perceived psychosocial support (PPS), which describes the belief that assistance is available to mitigate the effects of stressors. This study analyzes the association between PPS and resilience among Venezuelan migrant women using data from a cross-sectional study (2022) involving 9116 Venezuelan migrants aged 14 + . We hypothesized PPS and resilience would be positively correlated. Following the 'sensemaking' methodology, each participant shared a brief experience and completed a questionnaire contextualizing their experience. PPS and resilience were assessed using two single-item measures: one capturing how supported participants felt, and the other evaluating how often they believed they successfully coped with challenges. Using data from 5388 micro-narratives, we constructed a logistic regression model using backward elimination with inclusion at p < 0.20. Overall, 65% of participants self-reported resilience. The model included five of eight covariates: age, ethnicity, health issues, displacement duration, and relative wealth. Participants in the top tertile of PPS had 2.12 times the odds of resilience compared to those in the bottom tertile (95% CI: [1.84, 2.47], p < 0.0001), while the middle and bottom tertiles were equally resilient (OR=0.99, 95% CI: [0.87, 1.14], p = 0.91). Resilience correlated positively with age and relative wealth, and negatively with displacement duration and health issues. This study confirms PPS is important in the resilience of Venezuelan migrant women and elucidates several unexpected results for further investigation, including the null association between resilience and LGBTQ+ self-identification. Future studies should administer validated questionnaires to better understand the contributions of the constituent components of resilience among this population. These results can be utilized to develop tailored resilience-fostering interventions and more efficiently direct mental health resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".