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Objectives: This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods: Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results: Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion: The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Objectives: The primary objective of this study was to establish whether clinical depression and increased suicide risk differed between Australian fly-in, fly-out (FIFO) workers and their residential counterparts in the resources sector. We also sought to identify whether bullying and social support were associated with depression and suicide risk in this cohort. Methods: A cross-sectional survey design was used. Completed questionnaires were received from 751 respondents who were employed in the Australian resources sector. Primary outcomes were assessed with the Beck Depression Inventory, Beck Hopelessness Scale and Negative Acts Questionnaire – Revised. A general linear model was used to examine the association between depression, hopelessness and predictive factors. Results: The results of a general linear model analysis demonstrated that depression (partial η2 = 0.02; P = 0.01) and hopelessness (partial η2 = 0.02; P = 0.02) were significantly higher in residential than FIFO workers. In addition, bullying was significantly associated with higher levels of depression (partial η2 = 0.11; P = 0.001) and hopelessness (partial η2 = 0.04; P = 0.001). Finally, increased social support was significantly associated with lower depression rates (partial η2 = 0.13; P = 0.001) and hopelessness (partial η2 = 0.14; P = 0.001). Conclusions: Interventions should be delivered to reduce depression and hopelessness in Australian resource workers, particularly through addressing workplace bullying and enhancing social support. What is known about the topic?: One-third of Australian resource workers experience psychosocial distress. Research examining factors that influence such distress is lacking. What does this paper add?: The results of this study demonstrate that clinical depression and suicide risk were significantly higher in residential resource workers than in FIFO workers. Depression and suicide risk were directly associated with workplace bullying. In addition, social support was an important protective factor against depression and bullying in Australian FIFO and residential resource workers. What are the implications for practitioners?: There is an urgent need to introduce interventions that reduce psychosocial distress in the resource sector, particularly through the mitigation of bullying [ABSTRACT FROM AUTHOR]

Objective. This study aimed to assess and compare the psychological wellbeing of Australian hospital clinical staff at three timepoints during the coronavirus disease 2019 (COVID-19) pandemic. Methods. An anonymous, online, cross-sectional survey was conducted at three timepoints during the COVID-19 pandemic (T1: May–June 2020; T2: October–December 2020; T3: November 2021–January 2022). The surveys were completed by nurses, midwives, doctors and allied health staff employed at a large metropolitan tertiary health service located in Melbourne, Australia. The Depression, Anxiety and Stress Scale (DASS-21) assessed respondents’ psychological wellbeing in the past week. General linear models were used to measure the effects of survey timepoint on DASS-21 subscale scores, adjusting for selected sociodemographic and health characteristics. Results. A total of 1470 hospital clinical staff completed at least one survey (T1: 668 (14.7%), T2: 358 (7.9%) and T3: 444 (9.8%)). Respondents’ sociodemographic characteristics were similar across the three timepoints and within professional discipline groups. Respondents’ psychological wellbeing was worse at T3 compared to the earlier survey timepoints. Adjusting for respondent characteristics, depression, anxiety and stress scores were significantly higher for respondents of the third survey compared to the first (P < 0.001). Conclusions. There was a significant and persistent negative impact on the psychological wellbeing of hospital clinical staff in Australia across waves of the COVID-19 pandemic. Hospital clinical staff would benefit from ongoing and continued wellbeing support during and after pandemic waves. [ABSTRACT FROM AUTHOR]

There is increasing interest in the use of brief screening tools to improve detection of depression in the primary care setting. The aim of the present study was to compare the accuracy of the two-item Patient Health Questionnaire (PHQ-2) against the nine-item Patient Health Questionnaire (PHQ-9) for detecting depression among general practice patients. A cross-sectional sample of 3626 adults attending 12 Australian general practices was recruited. Participants completed the PHQ-2 and PHQ-9 via a touchscreen computer. Depression was defined as a PHQ-9 score ≥ 10. The area under the curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value were calculated. The PHQ-2 had good overall accuracy relative to the PHQ-9 for discriminating between cases and non-cases of depression, with an AUC of 0.92 (95% confidence interval 0.90-0.93). The PHQ-2 threshold of ≥3 was the best balance between sensitivity (91%) and specificity (78%) for detecting possible cases of depression. For clinical use, the optimal threshold was ≥2, with only 2% of possible cases missed. [ABSTRACT FROM AUTHOR]