Syndemic in a pandemic: An autoethnography of a COVID survivor
Bibliographic record
Abstract
This paper provides my personal experience as a COVID-19 survivor during and postrecovery periods. The stigma that my children and I underwent exposed us to the fragility of a social system that we struggle with all through our life to remain a part of. My story revealed a strong symbiotic relationship between the disease (COVID-19) and the patient's low acceptance in society, primarily attributed to misinformation and xenophobia around the COVID-19. This autoethnography speaks for several other COVID survivors who met with the same fate of being discriminated against and stigmatized. As a COVID patient and survivor, the traumatic experience was creating a fear psychosis in me, the effect of which I presume will stay beyond COVID-19. This condition of a syndemic seems to linger and negatively affect my outlook toward society. If COVID survivors develop a syndemic condition in a pandemic situation, it will require significant efforts to reserve it or sometimes even become irreversible.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".