Adversity and Resilience: The Stories of People Living With HIV in Ecuador
Bibliographic record
Abstract
People living with HIV (PLHIV) in Ecuador experience challenges including discrimination, violence, and limited access to healthcare, which impacts their mental health and well-being. However, research shows that PLHIV also rely on social resources to foster resilience. In the Ecuadorian context, there is no literature exploring these narratives, which results in a lack of qualitative data to improve the reality of PLHIV in the country. To gain a deeper understanding of these stories, 15 semi-structured interviews were undertaken (15 verbatim hours) within the context of a peer- and professional-led support group for PLHIV and were analyzed through a thematic approach based on Skovdal and Daniel's conceptual framework on resilience and adversity. Findings suggest that PLHIV face multifaceted challenges across the home, community, and political-economy spheres. Families and communities can elicit pain and fear, leading individuals to avoid discussing their diagnosis due to ongoing rejection. Further, discrimination perpetuated within the public health sector, as well as societal violence, exacerbates adversity. Nevertheless, participants stress the indispensable role of family support, community networks, and accessible healthcare in fostering resilience. Specifically, support, emotional reassurance, and willingness to learn enabled PLHIV to build resilience. These findings emphasize the need for approaches that counter discrimination, enhance well-being, ensure integral and intersectional healthcare access, and promote knowledge around HIV.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".