Clearing the air: protocol for a systematic meta-narrative review on the harms and benefits of e-cigarettes and vapour devices
Bibliographic record
Abstract
BACKGROUND: Under the shadow of the tobacco epidemic, the sale and use of e-cigarettes and other vapour devices is increasing dramatically. A contentious debate has risen within public health over the harms and benefits of these devices. Clearing the Air seeks to clarify the issues with a systematic review that informs the pressing regulatory and public health decisions to be made regarding these new products. METHODS/DESIGN: Using an integrated knowledge translation approach, public health researchers and knowledge users will work collaboratively throughout the project. Our research questions are the following: (1) What are the health risks and benefits of vapour devices, and how do these compare to cigarettes? (2) What is the harm reduction potential of vapour devices for individuals, the environment, and society? (3) Does youth vapour device experimentation lead to cigarette use? (4) Can vapour devices be effective aids for tobacco cessation? and (5) What is the potential toxicity of second-hand vapour? We are using meta-narrative review to synthesize studies from diverse research traditions because of its capacity to address contestations around a topic. The project has six phases. In the planning phase, we finalized the research questions. In the search phase, we are locating academic publications and grey literature aided by a research librarian. The mapping phase involves categorizing these papers into research traditions to understand different perspectives on the evidence for each research question. In the appraisal phase, we will select and evaluate the relevant papers. Finally, in the synthesis phase, using analytic techniques unique to meta-narrative methodology, we will compare and contrast the evidence from different research traditions to answer our research questions, identifying overarching meta-narratives. In the final stage, the full team will draft recommendations to be disseminated through a variety of knowledge translation strategies. DISCUSSION: Meta-narrative synthesis has the unique capacity to expose the debates that are influencing the interpretation of empirical studies on vapour devices. We seek to "clear the air" with an even-handed review of the evidence and an understanding of the tensions within public health so that we can offer clear-headed recommendations for policy, regulation, and future research. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015025267.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".