News Media Framing and Risk Communication: A Content Analysis of British Columbia’s 2014 Measles Outbreak
Bibliographic record
Abstract
Given their agenda setting function, the news media can play an important role in framing our understanding of health issues. Immunization in particular is considered a public health success story. Nonetheless, growing hesitancy towards immunization for a variety of reasons has resulted in outbreaks of vaccine preventable diseases (VPD) across North America. Using British Columbia’s 400-case measles outbreak in 2014, the present research employs a mixed method content analysis to examine news media framing of the outbreak in the Vancouver Sun and The Vancouver Province between March 1st-May 24th 2014. Key quantitative findings from the present study suggest that the dominant attribution of blame for the measles outbreak was religion (41%), medical/science sources were overwhelmingly relied upon in the coverage (80%), a greater degree of diligence was taken to avoid false balance, and finally there was a general lack of mobilizing information provided in the coverage. Key findings from the qualitative analysis suggest that while mandatory vaccination policies were seen as a positive solution to outbreaks, they could have polarizing implications. Thus, this study supports prior research calling for a national immunization registry. The present research concludes by presenting risk communication suggestions for public health authorities and the media.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".