The Comorbid Effects of Anxiety and Depressive Symptoms on COVID-19 Vaccine Hesitancy
Bibliographic record
Abstract
Background: Stringent public health interventions during the start of the COVID-19 pandemic highlighted the importance of vaccination as the exit strategy to return to normal life. Sociodemographic factors for vaccine hesitancy have been well established; however, literature on the psychological factors—namely anxiety and depressive symptoms—are limited and have conflicting results. This thesis studied the comorbid and independent effects of anxiety and depressive symptoms on vaccine hesitancy and explored time as an effect modifier for the relationships. Methods: Multi-wave national cross-sectional data collected by Mental Health Research Canada (MHRC) over the period of April 2021 to November 2021 was used for this study. GAD-7 and PHQ-9 were used to measure anxiety and depressive symptom levels, respectively. The prevalence of vaccine hesitancy was described. Multivariate logistic regression models were used to test the associations between anxiety/depressive symptoms and vaccine hesitancy, independently and then as an aggregate measure (psychological distress) to study the comorbid effects. Interaction analyses for the independent and comorbid effects model were done to test time as an effect modifier for the relationships of interest. Results: There were 11 232 participants for the independent effects analyses and 13 234 for the comorbid effects analyses. Vaccine hesitancy prevalence ranged from 26.8% in April 2021 to 7.4% by November 2021. Time was identified as an effect modifier for the independent effects of depressive symptoms and the comorbid effects of anxiety/depressive symptoms on vaccine hesitancy. Clinically significant anxiety symptoms were associated with vaccine hesitancy (OR=1.34, 95% CI: 1.12-1.59) but were not modified by time. Comorbid anxious depressed individuals were particularly affected in the month of August 2021 (OR=2.02, 95% CI: 1.44-2.83). Lower income and unemployment were associated with higher odds of vaccine hesitancy. Being 65+ years old and accessing mental health support were protective against vaccine hesitancy. Conclusions: Comorbid effects of anxiety/depressive symptoms on vaccine hesitancy were time-sensitive and coincided with the emergence of significant events during the COVID-19 pandemic. Further research is necessary to confirm these findings and test the issues of reverse causality and bidirectional association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".