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Record W4302287262 · doi:10.3389/fpsyt.2022.868032

The value of compassionate support to address smoking: A qualitative study with people who experience severe mental illness

2022· article· en· W4302287262 on OpenAlexaff
Kristen McCarter, Melissa L. McKinlay, Nadine Cocks, Catherine Brasier, Laura Hayes, Amanda Baker, David Castle, Ron Borland, Billie Bonevski, Catherine Segan, Peter J. Kelly, Alyna Turner, Jill M. Williams, John Attia, Rohan Sweeney, Sacha Filia, Donita Baird, Lisa Brophy

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsPsychological interventionMental healthQuitlineMental illnessSmoking cessationMedicinePeer supportQualitative researchRandomized controlled trialPopulationNicotine replacement therapySocial supportContext (archaeology)Intervention (counseling)PsychologyNursingPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Introduction: People experiencing severe mental illness (SMI) smoke at much higher rates than the general population and require additional support. Engagement with existing evidence-based interventions such as quitlines and nicotine replacement therapy (NRT) may be improved by mental health peer worker involvement and tailored support. This paper reports on a qualitative study nested within a peer researcher-facilitated tobacco treatment trial that included brief advice plus, for those in the intervention group, tailored quitline callback counseling and combination NRT. It contextualizes participant life experience and reflection on trial participation and offers insights for future interventions. Methods: = 14) following their 2-month (post-recruitment) follow-up assessments, which marked the end of the "Quitlink" intervention for those in the intervention group. Interviews explored the experience of getting help to address smoking (before and during the trial), perceptions of main trial components including assistance from peer researchers and tailored quitline counseling, the role of NRT, and other support received. A general inductive approach to analysis was applied. Results: We identified four main themes: (1) the long and complex journey of quitting smoking in the context of disrupted lives; (2) factors affecting quitting (desire to quit, psychological and social barriers, and facilitators and reasons for quitting); (3) the perceived benefits of a tailored approach for people with mental ill-health including the invitation to quit and practical resources; and (4) the importance of compassionate delivery of support, beginning with the peer researchers and extended by quitline counselors for intervention participants. Subthemes were identified within each of these overarching main themes. Discussion: The findings underscore the enormity of the challenges that our targeted population face and the considerations needed for providing tobacco treatment to people who experience SMI. The data suggest that a tailored tobacco treatment intervention has the potential to assist people on a journey to quitting, and that compassionate support encapsulating a recovery-oriented approach is highly valued. Clinical trial registration: The Quitlink trial was registered with ANZCTR (www.anzctr.org.au): ACTRN12619000244101 prior to the accrual of the first participant and updated regularly as per registry guidelines.

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)0.020
metaresearch head score (Gemma)0.021
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 score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.014
Scholarly communication0.0050.005
Open science0.0030.008
Research integrity0.0030.006
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 head0.015
GPT teacher head0.332
Teacher spread0.317 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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