Canadian newspaper coverage of the A/H1N1 vaccine program
Bibliographic record
Abstract
OBJECTIVES:The A/H1N1 mass vaccination program in Canada garnered considerable attention from the media, including extensive newspaper coverage. Media reports have been shown to influence the public's health care decisions, including vaccination choices. We analyzed Canadian newspapers' portrayal of the A/H1N1 vaccine including mention of risks and benefits of the vaccine and whether the article supported, questioned or was neutral about the vaccine. METHODS:We compiled a data set of Canadian newspaper articles (N = 234) and conducted a frequency content analysis to examine discussion and/or mention of evidence concerning vaccination, risks of the A/H1N1 virus and the vaccine, and tone of article in regards to the vaccination program in Canada. RESULTS:Reasons for getting vaccinated appeared in 71.8% of the articles, whereas only 18.4% provided reasons against getting vaccinated. Discussion of evidence to support claims for or against getting vaccinated appeared in only 27.8% and 6.8% of the articles, respectively. Risks associated with contracting the A/H1N1 virus were discussed in 49.6% of the articles and risks of the A/H1N1 vaccine were discussed in 12.4% of the articles. CONCLUSION:Newspaper coverage in Canada was largely supportive of the A/H1N1 mass vaccination program. However, serious risks associated with contracting the A/H1N1 virus were also frequently discussed in the print media. The news articles rarely presented direct evidence to support statements that the vaccine was safe, effective and properly tested. Known risks (such as potential allergic reactions and flu-like side effects) of the vaccine were rarely reported. The relationship between media portrayals and vaccine uptake warrants further research.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".