COVID-19 knowledge and mental health impact assessment in Haiti
Bibliographic record
Abstract
Mental health is a significant public health challenge globally, and one anticipated to increase following the COVID-19 pandemic. In many rural regions of developing nations, little is known about the prevalence of mental health conditions and factors that may help mitigate poor outcomes. This study assessed the impact of the COVID-19 pandemic on mental health and social support for residents of rural Haiti. Data were collected from March to May 2020. The Patient Health Questionnaire subscales for anxiety and depression, and the Perceived Stress Scale were utilized in addition to tailored questions specific to COVID-19 knowledge. Half (51.8%) of the 500 survey respondents reported COVID-19-related anxiety and worrying either daily or across a few days. Half (50.2%) also reported experiencing depression daily or across several days. Most (70.4%) did not have any social support, and 28.0% experienced some stress, with 13.4% indicating high perceived stress. Furthermore, 4.6% had suitable plumbing systems in their homes. The results were immediately actionable, informing the implementation of a mental health counseling program for youth following a loss of social support through school closures. Long-term investments must be made as part of public health responses in rural communities in developing nations, which remain under-studied.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".