Association of COVID-19 vaccination and anxiety symptoms: A Scleroderma Patient-centered Intervention Network (SPIN) Cohort longitudinal study
Bibliographic record
Abstract
OBJECTIVE: Symptoms of anxiety increased early in the COVID-19 pandemic among people with systemic sclerosis (SSc) then returned to pre-pandemic levels, but this was an aggregate finding and did not evaluate whether vaccination may have contributed to reduced anxiety symptom levels. We investigated whether being vaccinated for COVID-19 was associated with reduced anxiety symptoms among people with SSc. METHODS: The longitudinal Scleroderma Patient-centered Intervention Network (SPIN) COVID-19 Cohort was launched in April 2020 and included participants from the ongoing SPIN Cohort and external enrollees. Participants completed measures bi-weekly through July 2020, then every 4 weeks afterwards through August 2022 (32 assessments). We used linear mixed models to evaluate longitudinal trends of PROMIS Anxiety 4a v1.0 anxiety domain scores and their association with vaccination. RESULTS: Among 517 participants included in analyses, 489 (95%) were vaccinated by September 2021, and no participants were vaccinated subsequently. Except for briefly at the beginning, when few had received a vaccine, and end, when only 28 participants remained unvaccinated, anxiety symptom trajectories were largely overlapping. Participants who were never vaccinated had higher anxiety symptoms by August 2022, but there were no other differences, and receiving a vaccination did not appear to change anxiety symptom trajectories meaningfully. CONCLUSION: Vaccination did not appear to influence changes in anxiety symptoms among vulnerable people with SSc during the COVID-19 pandemic. This may be due to people restricting their behavior when they were unvaccinated and returning to more normal social engagement once vaccinated to maintain a steady level of anxiety symptoms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".