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Record W4382344605 · doi:10.1136/bmjopen-2022-070906

Experiences of nicotine users motivated to quit during the COVID-19 pandemic: a secondary qualitative analysis

2023· article· en· W4382344605 on OpenAlexafffundabout
Ramona H Sharma, Danielle Rodberg, L C Struik

Bibliographic record

VenueBMJ Open · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMinistry of Health, British ColumbiaCanadian Cancer Society
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthQualitative researchNicotineSmoking cessationFamily medicineMedical educationVirologyNursingPsychiatryInfectious disease (medical specialty)Internal medicineDiseasePathology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.231
GPT teacher head0.518
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes3
Has abstractyes

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