Community action for people with HIV and sex workers during the COVID-19 pandemic in India
Bibliographic record
Abstract
Sex workers have been one of the marginalized groups that have been particularly affected by India's stringent lockdown in response to the coronavirus disease 2019 (COVID-19) pandemic. The sudden loss of livelihood and lack of access to health care and social protection intensified the vulnerabilities of sex workers, especially those living with HIV. In response, Ashodaya Samithi, an organization of more than 6000 sex workers, launched an innovative programme of assistance in four districts in Karnataka. Since access to antiretroviral therapy (ART) was immediately disrupted, Ashodaya adapted its HIV outreach programme to form an alternative, community-led system of distributing ART at discreet, private sites. WhatsApp messaging was used to distribute information on accessing government social benefits made available in response to the COVID-19 pandemic. Other assistance included advisory messages posted in WhatsApp groups to raise awareness, dispel myths and mitigate violence, and regular, discreet phone check-ins to follow up on the well-being of members. The lessons learnt from these activities represent an important opportunity to consider more sustainable approaches to the health of marginalized populations that can enable community organizations to be better prepared to respond to other public health crises as they emerge.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".