THE EXPERIENCES OF OLDER ADULTS IN HAITI AND THE UNITED STATES AT THE ONSET OF COVID-19
Bibliographic record
Abstract
Abstract From the early onset of the COVID-19 pandemic, there was a disproportionate risk and impact of the disease on older adults and communities of color. This article explores the differences in experiences of older Haitian and older US adults at the onset of the COVID-19 pandemic. Since wealth creates accessibility to needed resources, including the best healthcare and support services, the disproportionate risk and impact of COVID-19 on older adults required a focused look at how older adults in vulnerable countries such as Haiti faired when compared to older adults in the United States. Using data collected from a sample of 240 Haitian older adults in August 2020 and 204 US older adults in June 2020, the findings indicated that Haitian older adults experienced more significant psychological, financial, and emotional distress due to the COVID-19 outbreak than their US older adult counterparts. The results of this study revealed the challenges that older adults faced at the onset of the pandemic, which was more pronounced in developing countries. However, the surge in violence in Haiti in the first quarter of 2023 killed more people than the COVID-19 outbreak. This research provides important insight for developing and framing policies and interventions that are not modeled on the measures taken elsewhere but adapted to a country’s reality and the population’s actual needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".