Disability-Related Risks Among Women and Girls Who Are Forcibly Displaced from Venezuela
Bibliographic record
Abstract
Our study aimed to explore the lived experiences of Venezuelan refugee/migrant women and girls with disabilities to guide humanitarian assistance. The data analysed was part of a larger cross-sectional study whereby refugees and migrants in Ecuador, Peru, and Brazil were asked to share the migration experiences of a Venezuelan woman or girl. The sample for this analysis was drawn from one of the survey questions that asked participants whether the woman/girl in the narrative identified as a person with a disability. Thematic analysis using inductive coding was performed. A total of 126 narratives were included in the final analysis, of which four major themes were identified. Venezuelan refugees and migrants with disabilities described experiences of discrimination, violence, and physical challenges, such as exacerbation of symptoms while in transit. In host countries, refugees and migrants experienced a lack of disability-related accommodations in the workplace and long wait times when trying to obtain healthcare. Since discrimination is a cross-cutting issue, human rights awareness highlighting the dignity of persons with disabilities is imperative. Resources and support for Venezuelan refugee and migrant women and girls with disabilities should aim to create accessible employment opportunities, safe and timely access to medical care, and prioritise violence prevention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".