Stepping into the Void: Lessons Learned from Civil Society Organizations during COVID-19 in Rio de Janeiro
Bibliographic record
Abstract
Brazil experienced some of the highest rates of COVID-19 globally. This was complicated by the fact that 35 million of its citizens have limited access to water, a primary resource necessary to stem the spread of infectious diseases. In many cases, civil society organizations (CSOs) stepped into this void left by responsible authorities. This paper explores how CSOs in Rio de Janeiro helped populations struggling with access to water, sanitation, and hygiene (WASH) during the pandemic, and what coping strategies are transferable to similar contexts. In-depth interviews (n = 15) were conducted with CSO representatives in the metropolitan region of Rio de Janeiro. Thematic analysis of the interviews revealed that COVID-19 exacerbated pre-existing social inequities among vulnerable populations, undermining their ability to protect their health. CSOs provided emergency relief aid but faced the counterproductive actions of public authorities who promoted a narrative that diminished the risks of COVID-19 and the importance of non-pharmacological interventions. CSOs fought this narrative by promoting sensitization among vulnerable populations and partnering with other stakeholders in networks of solidarity, playing a vital role in the distribution of health-promoting services. These strategies are transferrable to other contexts where state narratives oppose public health understandings, particularly for extremely vulnerable populations.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".