Management of smokers motivated to quit: a qualitative study of smokers and GPs
Bibliographic record
Abstract
BACKGROUND: The National Institute for Health and Clinical Excellence (NICE) guidelines state that GPs should manage smokers motivated to quit by offering referral to Stop Smoking Services (SSS) and that nicotine addiction treatment (NAT) should be offered only to those who decline referral. OBJECTIVE: To explore how smokers motivated to quit are managed in the GP consultation, specifically how treatment and referral are negotiated from the perspectives of both parties. METHODS: Twenty patients, identified in a consultation with their GP as motivated to quit smoking, and 10 participating GPs were interviewed. Interviews were recorded, transcribed, coded and analysed using the framework approach. RESULTS: Three strategies (treatment and follow-up by the GP, referral to SSS without treatment and immediate treatment with referral for follow-up) were evidenced in patient and GP accounts. Most patients were satisfied with their management and how this was negotiated, but some expressed surprise or dissatisfaction with lack of immediate treatment and questioned the need for referral to SSS. GPs welcomed the availability of SSS but some felt it important that they themselves also continued to support a quit attempt. Several saw advantages in offering NAT at the time the patient was motivated to stop. CONCLUSIONS: Smokers appear less convinced than GPs about the value of referral to SSS, although these differences may be resolved through negotiation. An alternative strategy to that proposed by NICE, which may be more acceptable to some smokers, is immediate treatment with subsequent support from SSS.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".