The Social Determinants of Health in the planning of COVID-19 testing in Amazonas, Brazil
Bibliographic record
Abstract
ABSTRACT The COVID-19 syndemic has disproportionately affected socially vulnerable populations, such as low-income individuals, Indigenous peoples, and riverine communities. Social Determinants of Health (SDH) have played a crucial role in the state of Amazonas, where unique geography and social disparities pose significant challenges to health access and equity. This article examines whether and how SDH were considered during COVID-19 testing planning in Amazonas. For this analysis, we conducted a qualitative case study through document analysis and semi-structured interviews with key stakeholders involved in testing planning and implementation. Official documents were systematized using TIDieR-PHP, and data were analyzed using the REFLEX-ISS tool. SDH were not considered in testing planning in Amazonas. The respondents could not all agree on the importance of considering SDH in intervention planning. Testing was limited to patients with severe symptoms and specific categories of essential workers. Health policymakers need to understand the relevance of considering SDH in planning population interventions to ensure equitable policy implementation.
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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 | 0.005 | 0.011 |
| 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 | 0.001 | 0.002 |
| 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".