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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".