“I am a young Venezuelan woman who left her country”: exploring the drivers of Venezuelan migration and how the migration experience impacts adolescent girls’ aspirations
Bibliographic record
Abstract
INTRODUCTION: Venezuela's geopolitical and economic crisis has forced many Venezuelans, including adolescent girls, to migrate. OBJECTIVE: To examine: (1) what prompted adolescent girls' decision to migrate and how future aspirations influenced that decision; and (2) how the migration experience impacted aspirations. The overall goal is to identify the unique needs of displaced Venezuelan adolescent girls to adapt programs and services to better support them in achieving their aspirations. METHODS: Migration experiences were collected in Ecuador, Peru, and Brazil in January to April of 2022 as part of the parent study. We conducted a qualitative thematic analysis of micronarratives from adolescent girls using an inductive approach. RESULTS: From the 188 micronarratives, themes of agency and aspirations for a better future were prominent, along with pregnancy being a motivator to migrate. Participants articulated their fears and how they navigated those, the lack of accessible resources, as well as what made them feel supported, like feeling welcomed and loved by those in the community. CONCLUSIONS: Although adolescent girls demonstrated resourcefulness and resilience, specific programs, and services to meet their unique needs are still necessary. Displaced girls are often vulnerable to barriers in accessing goods and services during the migration route and in their host community. Venezuelan adolescent girls need to be key stakeholders in interventions to develop effective programs that support them in every step of their migration journey.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".