Aim: To assess community optometristsÕ attitudes and current behaviour regarding provision of smoking cessation advice in their practice. Methods: A self-completion postal questionnaire was sent to community optometrists in north-west England identified from the General Optical Council's practice lists. Results: Of 709 optometrists identified, 71.8% (509/709) returned the completed questionnaire. Few community optometrists routinely asked about smoking habits: only 6.2% (95% CI: 4.1-8.3) (n ¼ 31) at new patient consultations, and 2.2% (95% CI: 0.9-3.5) (n ¼ 11) at follow-up visits. Reasons for optometrists not routinely providing smoking cessation advice included: not their role (35.4%, n ¼ 180), lack of time (22.0%, n ¼ 112) and forgetting to ask (21.4%, n ¼ 109). Overall 67.6% (95% CI: 63.5-71.7) (n ¼ 344) of community optometrists wanted to improve their knowledge of smoking and visual impairment with 56.2% (95% CI: 51.9-60.5) (n ¼ 286) requesting further training. Conclusion: Despite low levels of current involvement, many optometrists were keen to receive training on smoking cessation topics. We suggest that there are untapped opportunities to develop brief interventions to promote smoking cessation services in community optometry settings.
Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. In addition, text and layout data are evaluated in isolation. While a bimodal text and layout approach performs best (F1 = 0.684), we show that text is the most important single predictor of entity relations. Additionally, layout geometry is highly predictive and may even be a feasible unimodal approach. Despite being less effective, we highlight circumstances where visual information can bolster performance. In total, our results demonstrate the efficacy of training joint representations for RE.
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