The COVID-19 global pandemic and allostatic load among a cohort of Black and Latina transgender women living with HIV.
Bibliographic record
Abstract
This exploratory study investigated the impact of the COVID-19 pandemic on stress biomarkers and allostatic load for Black and Latina transgender women living with HIV (BLTWLH), as well as COVID-19 infection, hospitalization, and vaccination status. LITE Plus is a longitudinal cohort study of BLTWLH designed to identify pathways linking biopsychosocial stress to HIV co-morbidities. Participants were enrolled between October 2019-June 2022. Descriptive statistics compared stress biomarkers and allostatic load index (ALI) scores pre- (to March 2020) and post-onset pandemic onset (January 2021-December 2022). Frequencies and proportions are reported for COVID-19 indicators. Of the cohort, 26 BLTWLH completed study visits both pre- and post-onset pandemic onset ("pre-onset"; "post-onset"). Post-onset, chronic stress biomarkers were elevated across all body systems. Sample ALI distribution shifted post-onset, with elevated mean, median, IQR and proportion above the median. Of the 108 participants who completed any post-onset visits, 19% had ever tested positive for COVID-19 and 4% reported a COVID-19 related hospitalization. COVID-19 vaccination uptake was 70% and 24% had received a booster. Of those unvaccinated, 15% intended to be vaccinated, 9% were unsure and 6% did not intend to be vaccinated. BLTWH deployed various strategies to cope with pandemic effects and 22% reported unmet COVID-19-related support needs. ALI for BLTWLH was high compared to other populations in the literature, suggesting unique vulnerabilities to biopsychosocial stress and chronic disease risk. Despite high engagement with COVID-19 prevention including vaccination intention and uptake, BLTWLH experienced heavy COVID-19 burden and unmet support needs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".