A Content Analysis of Newspaper Coverage of the Seasonal Flu Vaccine in Ontario, Canada, October 2001 to March 2011
Bibliographic record
Abstract
Seasonal flu vaccine uptake has fallen dramatically over the past decade in Ontario, Canada, despite promotional efforts by public health officials. Media can be particularly influential in shaping the public response to seasonal flu vaccine campaigns. We therefore sought to identify the nature of the relationship between risk messages about getting the seasonal flu vaccine in newspaper coverage and the uptake of the vaccine by Ontarians between 2001 and 2010. A content analysis was conducted to quantify risk messages in newspaper content for each year of analysis. The quantification allowed us to test the correlation between the frequency of risk messages and vaccination rates. During the time period 2001-2010, vaccination rates were positively and significantly related to the frequency of risk messages in newspaper coverage (r = .691, p < .05). The most commonly identified risk messages related to the flu vaccine being ineffective, the flu vaccine being poorly understood by science, and the flu vaccine causing harm. Newspaper coverage plays an important role in shaping public response to seasonal flu vaccine campaigns. Public health officials should work alongside media to ensure that the public are exposed to information necessary for making informed decisions regarding vaccination.
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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 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".