When Pictures Waste a Thousand Words: Analysis of the 2009 H1N1 Pandemic on Television News
Bibliographic record
Abstract
OBJECTIVES: Effective communication by public health agencies during a pandemic promotes the adoption of recommended health behaviours. However, more information is not always the solution. Rather, attention must be paid to how information is communicated. Our study examines the television news, which combines video and audio content. We analyse (1) the content of television news about the H1N1 pandemic and vaccination campaign in Alberta, Canada; (2) the extent to which television news content conveyed key public health agency messages; (3) the extent of discrepancies in audio versus visual content. METHODS: We searched for "swine flu" and "H1N1" in local English news broadcasts from the CTV online video archive. We coded the audio and visual content of 47 news clips during the peak period of coverage from April to November 2009 and identified discrepancies between audio and visual content. RESULTS: The dominant themes on CTV news were the vaccination rollout, vaccine shortages, long line-ups (queues) at vaccination clinics and defensive responses by public health officials. There were discrepancies in the priority groups identified by the provincial health agency (Alberta Health and Wellness) and television news coverage as well as discrepancies between audio and visual content of news clips. Public health officials were presented in official settings rather than as public health practitioners. CONCLUSION: The news footage did not match the main public health messages about risk levels and priority groups. Public health agencies lost control of their message as the media focused on failures in the rollout of the vaccination campaign. Spokespeople can enhance their local credibility by emphasizing their role as public health practitioners. Public health agencies need to learn from the H1N1 pandemic so that future television communications do not add to public confusion, demonstrate bureaucratic ineffectiveness and contribute to low vaccination rates.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".