Browsing by Author "Moore A"

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    A mixed-methods evaluation of an intervention for enhancing alcohol screening in adults aged 50+ attending primary health care
    (CSIRO Publishing on behalf of The Royal New Zealand College of General Practitioners, 2025-01-02) Towers A; Newcombe D; White G; McMenamin J; Sheridan J; Rahman J; Moore A; Stokes T
    Introduction Adults aged 50 years and over are drinking more than ever but primary health care (PHC) professionals find it challenging to screen them for alcohol-related harm, despite being at greater risk for harm than younger drinkers. Aim This intervention aimed to enhance alcohol screening for this cohort by (a) introducing an algorithm in the patient management system to automate detection of alcohol risk in patients and (b) providing training to support health professionals' practice of, knowledge about, and comfort with alcohol screening in this cohort. Methods Eleven PHC practices in Aotearoa New Zealand took part in this intervention, including 41 PHC health professionals. Development and integration of the automated alcohol screening process within PHC patient management systems was undertaken in parallel with health professional training approaches. Results Screening rates increased substantially at intervention initiation but fell immediately with the onset of the New Zealand COVID-19 national lockdown. Two-thirds of health professionals identified the system screening prompts, over 40% felt this changed their screening practice, and 33% increased their awareness of - and felt more comfortable screening for - alcohol-related risk in those aged 50+. Discussion We illustrated an initial increase in alcohol screening rates in those aged 50+ as a result of this intervention, but this increase could not be sustained in part due to COVID-19 disruption. However, health professionals indicated that this intervention helped many change their practice and enhanced their awareness of such risk and comfort in screening for alcohol-related risk in those aged 50+.
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    Charting just futures for Aotearoa New Zealand: philosophy for and beyond the Covid-19 pandemic
    (Taylor and Francis Group, 2021-03) Mulgan T; Enright S; Grix M; Jayasuriya U; Ka‘ili TO; Lear AM; Māhina ANM; Māhina Ō; Matthewson J; Moore A; Parke EC; Schouten V; Watene K
    The global pandemic needs to mark a turning point for the peoples of Aotearoa New Zealand. How can we make sure that our culturally diverse nation charts an equitable and sustainable path through and beyond this new world? In a less affluent future, how can we ensure that all New Zealanders have fair access to opportunities? One challenge is to preserve the sense of common purpose so critical to protecting each other in the face of Covid-19. How can we centre what we have learnt about resilience within Māori and wider Pacific communities in our reforms? How can public understanding of Covid-19 science create a platform for the future social valuing of expertise? How can we ensure that the impact of Covid-19 in New Zealand results in a more sustainable, and inclusive workforce – for instance by expanding our perceptions of the value of our workers through promoting digital inclusion? To meet these challenges, we must reimagine our existing traditions of thought, breathing new life into perennial concepts and debates. Our paper indicates some of the ways that Philosophy is central to this collective reimagining, highlighting solutions to be found across our rich philosophical traditions.
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    Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2025-08-15) Wijegunarathna K; Stock K; Jones CB; Sila-Nowicka K; Moore A; O’Sullivan D; Adams B; Gahegan M
    Millions of biological sample records collected in the last few centuries archived in natural history collections are un-georeferenced. Georeferencing complex locality descriptions associated with these collection samples is a highly labour-intensive task collection agencies struggle with. None of the existing automated methods exploit maps that are an essential tool for georeferencing complex relations. We present preliminary experiments and results of a novel method that exploits multimodal capabilities of recent Large Multi-Modal Models (LMM). This method enables the model to visually contextualize spatial relations it reads in the locality description. We use a grid-based approach to adapt these auto-regressive models for this task in a zero-shot setting. Our experiments conducted on a small manually annotated dataset show impressive results for our approach (∼1 km Average distance error) compared to uni-modal georeferencing with Large Language Models and existing georeferencing tools. The paper also discusses the findings of the experiments in light of an LMM's ability to comprehend fine-grained maps. Motivated by these results, a practical framework is proposed to integrate this method into a georeferencing workflow.