Assessing the impact of COVID-19 on Toronto's Spanish-speaking Latin American population: Qualitative study
Bibliographic record
Abstract
Background: The COVID-19 pandemic has intensified pre-existing health, social, and economic disparities in Canada, particularly affecting racialized, immigrant, refugee, and newcomer communities. While existing research indicates that Latin Americans have been disproportionately impacted by the pandemic, questions remain about why this group faces greater risk and worse health and other outcomes compared with the rest of the population. Despite knowledge of inequities in Toronto and elsewhere, research remains limited on the perspectives and experiences of specific communities throughout the pandemic. Methods: This qualitative research focuses on the experiences of Spanish-speaking Latin Americans in Toronto who contracted COVID-19, had family members and friends who were sick from the virus, and/or provided services to Latin Americans in the city during the pandemic. Results: The study highlights challenges related to informal caregiving, language barriers in accessing healthcare, digital inequities, and difficulties faced by individuals with precarious immigration status. Conclusion: Understanding the experiences of Spanish-speaking Latin Americans in Toronto can help identify necessary support and services to address these inequities in a post-pandemic scenario.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".