Justice implications of health and food security policies for Indigenous peoples facing COVID-19: a qualitative study and policy analysis in Peru
Bibliographic record
Abstract
The spread of COVID-19 in Peru resulted in the declaration of a national health emergency, in which Indigenous peoples were identified as being particularly vulnerable due to their pre-existing poor health indicators and disadvantaged social conditions. The aim of this paper is to examine how the Peruvian government responded to the health and food needs of the Shawi and Ashaninka Indigenous peoples of Peru during the first 18 months of the pandemic (March 2020-August 2021). This study uses both official policy documents and real-world experiences to evaluate policy responses in terms of their immediate impact and their longer-term sustainability and contribution to the improvement of health, well-being and justice for Indigenous communities. Four health and food security responses were evaluated: the Amazon Health Plan and Indigenous Command; food aid; cash aid; and COVID-19 vaccination. We employed the Multidimensional Injustice Framework to analyse the justice implications of the design and implementation of responses. Data collection included 71 interviews with government officials (n = 7), Indigenous leaders (n = 31) and community members (n = 33). The results show how national and regional governments released policies to address the health and food needs of Indigenous peoples directly or indirectly, as part of a broader focus on vulnerable people. However, justice implications were not sufficiently addressed in the design or implementation of the responses. On the distributive dimension, Indigenous communities were prioritized to receive health goods and services, nevertheless, the distribution had shortcomings that impeded their collection and Indigenous food systems and livelihoods were largely overlooked. On the procedural dimension, Indigenous representatives were included to provide culturally sensitive feedback on health interventions, but without funding, and furthermore, the community members had only passive participation. This paper points out the importance of considering and addressing justice implications for more effective and fairer health and food policy responses to current and future health crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".