A ‘Cut-Down-To-Stop’ intervention for smokers who find it hard to quit: a qualitative evaluation
Bibliographic record
Abstract
BACKGROUND: English Stop Smoking Services primarily deliver behavioural interventions to support abrupt quit attempts. Recent evidence suggests an alternative approach could be offered to clients involving a more gradual reduction of cigarettes smoked leading to complete abstinence, known as 'Cut Down To Stop' (CDTS). The purpose of this study was to explore the experiences of stop smoking practitioners and service users who participated in a pilot study of a CDTS service. METHODS: The CDTS intervention was pilot tested in a Stop Smoking Service in London, England. As part of the CDTS intervention clients who were still smoking 2 weeks after their quit date were offered tailored advice, medication and support to reduce their current smoking by half, with the aim to stop smoking altogether within a six-month period. A qualitative evaluation was conducted involving a focus group discussion with nine practitioners involved in the delivery of the CDTS intervention and telephone interviews with 18 CDTS service users. Thematic analysis was performed. RESULTS: Service users and practitioners were very positive about their experience with the CDTS intervention. The intervention was found to be an effective way of keeping clients engaged with the service and was felt to increase the likelihood they might quit and/or re-engage in service for future quit attempts. Elements that contributed to the attractiveness of the CDTS intervention included: 1) the trust and empathetic relationship developed between service users, practitioners and their referring primary care provider; 2) time and flexibility for service users to engage in the quitting process at their own pace; 3) setting progressive goals and building service user confidence; 4) the opportunity to experiment with quit smoking medications; and, 5) the on-going contact with the practitioner/service. CONCLUSIONS: Service users who are not successful with quitting abruptly may benefit from a CDTS intervention. This study highlights the important role of 'relationships', time and 'flexible' service delivery models in engaging service users who are not initially successful with quitting. The findings of this study have the potential to inform decision-making regarding the value of the CDTS approach for the English Stop Smoking Service and cessation services worldwide.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".