'Very brief advice' (VBA) on smoking in family practice: a qualitative evaluation of the tobacco user's perspective.

BACKGROUND: Very Brief Advice on smoking (VBA) is an evidence-based intervention designed to increase quit attempts among patients who smoke. VBA has been widely disseminated in general practice settings in the United Kingdom, however its transferability to Southern European settings is not well established. This study sought to document the perspectives of Greek general practice patients in terms of the acceptability and satisfaction with receiving VBA from their general practitioner (GP) and its influence on patients' motivation to make a quit attempt. We also examine patient identified barriers and facilitators to acting on VBA. METHODS: Semi-structured interviews were conducted with 50 patients who reported current tobacco use recruited from five general practices in Crete, Greece. All patients received VBA from their GP and interviews were conducted immediately after the GP appointment. Thematic analysis was used to analyze data. RESULTS: The majority of patients were satisfied with the VBA intervention. Approximately one quarter of patients reported they were motivated to make an attempt to quit smoking after receiving VBA from their GP. Patients identified a clear preference for VBA to be delivered in a supportive manner, which communicated genuine concern versus fear-based approaches. Patients with an existing smoking-related illness were more likely to report plans to act on their GP's VBA. Patients not ready to quit smoking indicated they would be likely to seek the support of their GP for future quit attempts as a result of VBA. Many patients reported low self-efficacy with quitting and apprehension about available quit smoking supports. CONCLUSIONS: VBA was positively received by the majority of smokers interviewed. Participating patients confirmed the motivational role of advice when delivered in a supportive and caring manner. Personal health status, beliefs about quit smoking supports, and low self-efficacy appear to influence patient's motivation to make an aided quit attempt.

Augmentation by Counterfactual Explanation - Fixing an Overconfident Classifier.

A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect a high uncertainty on ambiguous in-distribution samples that lie close to the decision boundary. The model should also refrain from making overconfident decisions on samples that lie far outside its training distribution, far-out-of-distribution (far-OOD), or on unseen samples from novel classes that lie near its training distribution (near-OOD). This paper proposes an application of counterfactual explanations in fixing an over-confident classifier. Specifically, we propose to fine-tune a given pre-trained classifier using augmentations from a counterfactual explainer (ACE) to fix its uncertainty characteristics while retaining its predictive performance. We perform extensive experiments with detecting far-OOD, near-OOD, and ambiguous samples. Our empirical results show that the revised model have improved uncertainty measures, and its performance is competitive to the state-of-the-art methods.