Reactions on Twitter towards Australia's proposed import restriction on nicotine vaping products: a thematic analysis
Bibliographic record
Abstract
OBJECTIVE: In June 2020, the Australian Government announced that personal importation of nicotine vaping products (NVP) would be prohibited, pending a 12-month classification and regulation review by the Therapeutic Goods Administration. This brief report examines the themes of responses on Twitter to this announcement. METHODS: Simple random sampling was used to retrieve tweets containing keywords from 19 to 26 June 2020. Tweets were manually coded and descriptive statistics calculated for themes and policy position. RESULTS: The vast majority of the 1,168 tweets were anti-policy. Themes included: criticism towards government (59.8%), activism against NVP restriction (38%), potential adverse consequences (30.8%) and support for NVP restriction (1.4%). Tweets that identified potential adverse consequences of NVP restriction cited: smoking relapse for individuals currently using NVPs (75.6%); the impact of policy enforcement (8.6%); illicit market (8.3%); panic buying (3.6%); difficulty obtaining prescriptions (2.8%); and impacts on NVP businesses (2.8%). CONCLUSION: Tweets predominately objected to the policy announcement. Approximately three-quarters of tweets that cited potential adverse consequences of the policy mentioned smoking relapse as their primary concern. Implications for public health: User-generated content on Twitter was primarily used to lobby against the proposed policy, which was subsequently amended.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".