A narrative-based approach to understand the impact of COVID-19 on the mental health of stranded immigrants in four border cities in Mexico
Bibliographic record
Abstract
Objective: This paper describes the impact that the different COVID-19 related restrictions have had on the mental health and wellbeing of 57 Central American and Caribbean immigrants stranded in Mexico due to the pandemic. Methods: Ethnographic data was obtained through the application of in-depth interviews centered on topics such as migration history, personal experience with COVID-19 and beliefs about the pandemic. This information was further analyzed through a narrative approach and Atlas Ti. Main findings: US Title 42 and the Migrant Protection Protocols (MPP) have stranded thousands of individuals in the US-Mexico border region, a situation that has overcrowded the available shelters in the area and forced many of the immigrants to live on the streets and in improvised encampments. Thus, exposing them to a higher risk of contagion. Furthermore, the majority of the interviewed Central American and Caribbean immigrants consider that Mexico is more lenient when it comes to the enforcement of sanitary measures, especially when compared to their countries of origin. Finally, vaccination hesitancy was low among the interviewees, mainly due to the operative aspects of the vaccination effort in Mexico and the fear of ruining their chances to attain asylum in the US. These findings are backed up by the discovery of five recurring narratives among the interviewees regarding: (1) The pandemic's psychological impact. (2) The uncertainty of being stranded in Mexico and the long wait. (3) Their fear of violence over the fear of contagion. (4) The perceived leniency of Mexico with the pandemic when compared to their countries of origin, and (5) their beliefs about the pandemic and vaccines. Key finding: The mental health of stranded Central American and Caribbean immigrants in Mexico during the COVID-19 pandemic is mostly affected by their inability to make it across the US-Mexico border using legal means.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".