Uncovering Public Perceptions of Older Adults’ Vaccines in Canada: A Study of Online Discussions from National Media Sources
Bibliographic record
Abstract
This study explored how a subsection of Canadians perceive older adults' vaccines through a qualitative analysis of comments posted in response to national online news articles. We used reflexive thematic analysis to analyse 147 comments from 31 news article comments sections published between 2015 and 2020 from five different national online news sources (CBC, National Post, Global News, Globe & Mail, and Huffington Post Canada) that focused on three older adults' diseases and vaccines: influenza, pneumococcal pneumonia, and herpes-zoster. Three themes encompassed the similarities and differences in how these three diseases were discussed: (1) the importance of personal experiences on stated stance in vaccine uptake or refusal, (2) questioning vaccine research and recommendations, and (3) criticisms of the government's unequal vaccine opportunities across different Canadian provinces. Our findings identified that perceptions regarding older adult vaccination were dependent on the vaccine type, and, therefore, we make suggestions for future researchers to build on our findings, particularly the need not to treat the research subject of "older adults' vaccines" as one entity. Gaining a better understanding of how older adults' vaccines are perceived in Canada will enable public health professionals to develop effective communication strategies that should ultimately improve vaccination rates for older adults.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".