Experiences of nicotine users motivated to quit during the COVID-19 pandemic: a secondary qualitative analysis
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has brought to light a variety of key factors that affect tobacco use, including behavioural patterns, social support and connection, and physical and mental health. What we do not know is how those motivated to quit were impacted by the pandemic. As such, understanding the unique experiences and needs of people motivated to quit smoking or vaping during the COVID-19 pandemic is critical. The aim of this study was to examine the cessation experiences of nicotine users during the COVID-19 pandemic. DESIGN: We conducted a supplementary secondary analysis of primary qualitative data, i.e., semi-structured interviews with individuals engaged in cigarette use (smoking), e-cigarette use (vaping) and dual use. SETTING: British Columbia, Canada. PARTICIPANTS: Relevant data were drawn from 33 participants out of the primary study's 80-participant sample pool. MEASURES: Interview questions explored barriers and facilitators to quitting nicotine use. We then used conventional content analysis to identify relevant and additional emergent themes and subthemes surrounding pandemic-specific barriers and facilitators to quitting, and unique needs for cessation support in the context of the COVID-19 pandemic. RESULTS: Pandemic-specific barriers included lifestyle limitations and poor mental health due to isolation. Facilitators to quitting during the pandemic included reduced access and opportunities to use nicotine products, as well as time for personal reflection on nicotine use behaviours. Suggestions for cessation programming included a primary focus on enhancing social support features (e.g., discussion forums, support groups), followed by increasing awareness of the benefits of quitting, and enhancing visibility of resources available to support quitting. CONCLUSIONS: The findings provide directions for how cessation supports can be tailored to better meet the needs of users motivated to quit during and beyond the COVID-19 pandemic.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".