Meningococcal disease in Italy: public concern, media coverage and policy change
Bibliographic record
Abstract
BACKGROUND: Between 2015 and 2017 six deaths due to meningitis in the Lombardy Region, Northern Italy, caught the attention of media and increased concern among the population, with a consequent increase in demand for vaccination. Considering the evidence about the impact of media coverage of health issues on public behaviour, this paper investigates the trend of media coverage and internet searches regarding meningitis in the Lombardy Region. METHODS: Content analysis of online articles published from January 2015 to May 2017 and analysis of Google Trends were carried out. A codebook was created in order to assess the content of each article analysed, based on six areas: article characteristics, information about meningococcal disease and vaccination, Local Health Authority activities, accuracy of information and tone of the message. RESULTS: Both public interest and media attention peaked in December 2016 and January 2017, when the Lombardy Regional Authority changed its policy by offering co-payment to adults with a saving of 50%. The frequency of meningitis coverage decreased after the announcement of policy change. For example, articles containing new information on meningitis or meningococcal vaccine (76 to 48%, p = 0.01) and preventive recommendations (31% down to 10%, p = 0.006) decreased significantly. An alarmist tone appeared in 21% of pre-policy articles that decreased to 5% post-policy (p = 0.03). CONCLUSIONS: The findings suggest a role for the media in fostering public pressure towards health services and policy-makers. A collaboration between Public Health institutions and the media would be beneficial in order to improve communication with the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".