Influenza vaccination discourse in major Canadian news media, 2017–2018
Bibliographic record
Abstract
Influenza vaccine uptake is less-than-ideal in many jurisdictions, including Canada. In this study we sought to assess news articles relating to influenza vaccination by major Canadian newspapers during a six-month period relatively congruent to the seasonal influenza outbreak for 2017-2018. We identified 116 unique articles published between August 16, 2017 and February 15, 2018, then developed and applied a coding frame to them. Influenza vaccination was portrayed primarily positively (74.14%), sometimes negatively (14.66%), and occasionally neutrally (11.21%). Articles were most commonly focused on news about the prevalence, or amount of harm/death caused by, the influenza virus (31.03%), or on public announcements primarily concerning influenza vaccination (17.24%). Benefits of influenza vaccination were often stated (59.48%), most commonly including reduction in disease (47.41%) and protection of vulnerable individuals (26.72%). Issues or problems with influenza vaccination were also often stated (55.17%), most commonly relating to low or non-effectiveness of the vaccine (43.10%). Most articles stated that people should get vaccinated (65.52%). Canadian newspaper articles generally support the scientific consensus that influenza vaccination is a highly positive intervention. Nonetheless, a clear picture of the true value of influenza vaccination may sometimes be missing in articles focusing on low effectiveness and lacking any mention of vaccination's positive value. Overall, we can reasonably conclude that, in Canada, misinformation and antivaccination rhetoric are coming primarily from sources other than newspapers.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".