What the Public Was Saying about the H1N1 Vaccine: Perceptions and Issues Discussed in On-Line Comments during the 2009 H1N1 Pandemic
Why this work is in the frame
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Bibliographic record
Abstract
During the 2009 H1N1 pandemic, a vaccine was made available to all Canadians. Despite efforts to promote vaccination, the public's intent to vaccinate remained low. In order to better understand the public's resistance to getting vaccinated, this study addressed factors that influenced the public's decision making about uptake. To do this, we used a relatively novel source of qualitative data--comments posted on-line in response to news articles on a particular topic. This study analysed 1,796 comments posted in response to 12 articles dealing with H1N1 vaccine on websites of three major Canadian news sources. Articles were selected based on topic and number of comments. A second objective was to assess the extent to which on-line comments can be used as a reliable data source to capture public attitudes during a health crisis. The following seven themes were mentioned in at least 5% of the comments (% indicates the percentage of comments that included the theme): fear of H1N1 (18.8%); responsibility of media (17.8%); government competency (17.7%); government trustworthiness (10.7%); fear of H1N1 vaccine (8.1%); pharmaceutical companies (7.6%); and personal protective measures (5.8%). It is assumed that the more frequently a theme was mentioned, the more that theme influenced decision making about vaccination. These key themes for the public were often not aligned with the issues and information officials perceived, and conveyed, as relevant in the decision making process. The main themes from the comments were consistent with results from surveys and focus groups addressing similar issues, which suggest that on-line comments do provide a reliable source of qualitative data on attitudes and perceptions of issues that emerge in a health crisis. The insights derived from the comments can contribute to improved communication and policy decisions about vaccination in health crises that incorporate the public's views.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it