Public Attitudes Towards Vaccine Mandates in Alberta During the ‘Pandemic of the Unvaccinated’: A Qualitative Analysis of Reddit Posts (Preprint)
Bibliographic record
Abstract
BACKGROUND During a period that was referred to as the “pandemic of the unvaccinated” in Alberta, the province had among the lowest vaccination rates in the country and four times the national average of cases. As the number of COVID-19 cases and hospitalizations reached their peak, governments resisted implementing vaccine measures. Users of social media platforms, such as Reddit, posted their discourse about these events in real time, providing a perspective of public attitudes towards public health action or lack thereof. This paper examines the content of posts, applying a qualitative lens to understand the themes underlying those responses. OBJECTIVE The goal of this study is to understand the attitudes and beliefs towards mandatory vaccination policies in Alberta, Canada in September 2021, during the fourth wave of COVID-19. METHODS 9400 posts between September 1st and September 30th, 2021 were collected from the subreddit r/Alberta with Pushshift.io. Posts and comments were manually screened to determine their relevance to research objectives, and then coded using inductive coding methods. RESULTS Inductive coding methods yielded five key themes: (i) sentiments related to autonomy and consent, (ii) concerns about COVID-19 vaccine passport enforcement, (iii) concerns about government, (iv) concerns about the logistics of passports, and (v) sentiments relating to the necessity of passports to prevent lockdowns. CONCLUSIONS Overall, the data presented favorable sentiments towards an Albertan vaccine passport. Anti-vaccine and anti-mandate sentiments were often less extreme than those present in the literature, although this may be due to r/Alberta subreddit moderators removing those more extreme comments. Most reservations were due to issues of bodily autonomy, though concerns about the government and logistics also played a meaningful role.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".