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Mediatised public crisis and diasporic Jewish identity: the Malka Leifer case in Australian mainstream and local religious news coverage, and community talk
No full textTowards the enabling museum: exploring accessibility with adult visitors experiencing blindness or low vision
No full textPriority-aware convolutional neural networks for multiclass image classification in healthcare applications
No full textBacterial skin and soft tissue infections: exploring epidemiology, management challenges, and novel alternative therapies in the era of rising antimicrobial resistance
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Evaluation of dietary interventions as adjunctive treatments for individuals living with major depressive disorder
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Changes in Mental State for Help-Seekers of Lifeline Australia’s Online Chat Service:Lexical Analysis Approach
Get PDFBackground: Mental health challenges are escalating globally, with increasing numbers of individuals accessing crisis helplines through various modalities. Despite this growing demand, there is limited understanding of how crisis helplines benefit help-seekers over the course of a conversation. Affective computing has the potential to transform this area of research, yet it remains relatively unexplored, partly due to the scarcity of available helpline data. Objective: This study aimed to explore the feasibility of using lexical analysis to track dynamic changes in the mental state of help-seekers during online chat conversations with a crisis helpline. Methods: Lexical analysis was conducted on 6618 deidentified online chat transcripts collected by Lifeline Australia between April and June 2023 using the validated Empath lexical categories of Positive Emotion, Negative Emotion, Suffering, and Optimism. Furthermore, 2 context-specific categories, Distress and Suicidality, were also developed and analyzed to reflect crisis support language. Correlation analyses evaluated the relationships between the 6 lexical categories. One-way ANOVAs assessed changes in each lexical category across 3 conversation phases (beginning, middle, and end). Trend analyses using regression modeling examined the direction and strength of changes in lexical categories across 9 overlapping conversation windows (20% size and 50% step overlap). Results: Significant changes were observed across conversation phases. The context-specific categories showed the strongest improvements from the beginning to end phase of conversation, with a large reduction in Distress (d=0.79) and a moderate reduction in Suicidality (d=0.49). The most frequently occurring terms representing Distress were “hard,” “bad,” and “down,” and for Suicidality were “suicide,” “stop,” and “hurt.” The negatively framed Empath categories also significantly reduced, with moderate effect sizes for Suffering (d=0.49) and Negative Emotion (d=0.39). There were also significant but small reductions in the positively framed Empath categories of Positive Emotion (d=0.15) and Optimism (d=0.07) from the beginning to end phase of conversation. Correlation coefficients indicated the lexical categories captured related but distinct constructs (r=.34 to r=0.82). Trend analyses revealed a consistent downward trajectory across most lexical categories. Distress showed the steepest decline (slope=−0.15, R 2=0.97), followed by Suffering (slope=−0.11, R 2=0.96), Negative Emotion (slope=−0.10, R 2=0.69), and Suicidality (slope=−0.06, R 2=0.88). Positive Emotion showed a slight negative trend (slope=−0.04, R 2=0.54), while Optimism remained relatively stable across the conversation windows (slope=0.01, R 2=0.13). Conclusions: This study demonstrates the feasibility of using lexical analysis to represent and monitor mental state changes during online crisis support interactions. The findings highlight the potential for integrating affective computing into crisis helplines to enhance service delivery and outcome measurement. Future research should focus on validating these findings and exploring how lexical analysis can be applied to improve real-time support to those in crisis.</p
Methodology used to develop the minimum common data elements for surveillance and Reporting of Musculoskeletal Injuries in the MILitary (ROMMIL) statement
No full textBackgroundThe objective was to summarize the methodology used to reach consensus for recommended minimum data elements that should be collected and reported when conducting injury surveillance research in military settings. This paper summarizes the methodology used to develop the international Minimum Data Elements for surveillance and Reporting of Musculoskeletal Injuries in the MILitary (ROMMIL) statement.MethodsA Delphi methodology was employed to reach consensus for minimum reporting elements. Preliminary steps included conducting a literature review and surveying a convenience sample of military stakeholders to 1) identify barriers and facilitators of military musculoskeletal injury (MSKI) prevention programs, 2) identify relevant knowledge/information gaps and 3) establish future research priorities. The team then led a sequential three-round Delphi consensus survey, including relevant stakeholders from militaries around the world, and then conducted asynchronous mixed knowledge user meeting to explore level of agreement among subject matter experts. Knowledge users, including former and current military service members, civil servant practitioners, and global-wide subject matter experts having experience with policy, execution, or clinical investigation of MSKI mitigation programs, MSKI diagnoses, and MSKI risk factors in military settings. For each round, participants scored each question on a Likert scale of 1-5. Scores ranged from No Importance (1) to Strong Importance (5).ResultsLiterature review and surveys helped informed the scope of potential variables to vote on. Three rounds were necessary to reach minimum consensus. Ninety-five, 65 and 42 respondents participated in the first, second and third rounds of the Delphi consensus, respectively. Ultimately, consensus recommendations emerged consisting of one data principle and 33 minimum data elements.ConclusionsAchieving consensus across relevant stakeholders representing military organizations globally can be challenging. This paper details the methodology employed to reach consensus for a core minimum data elements checklist for conducting MSKI research in military settings and improve data harmonization and scalability efforts. These methods can be used as a resource to assist in future consensus endeavors of similar nature.<br/
EditIQ: Automated Cinematic Editing of Wide-Angle Videos via Dialogue Interpretation and Saliency Cues:Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues
No full textWe present EditIQ, a completely automated framework for cinematically editing scenes captured via a stationary, large field-of-view and high-resolution camera. From the static camera feed, \ourmethod~ initially generates multiple virtual feeds, emulating a team of cameramen. These virtual camera shots termed \textit{rushes} are subsequently assembled using an automated editing algorithm, whose objective is to present the viewer with the most vivid scene content. To understand key scene elements and guide the editing process, we employ a two-pronged approach: (1) a large language model (LLM)-based dialogue understanding module to analyze conversational flow, coupled with (2) visual saliency prediction to identify meaningful scene elements and camera shots therefrom. We then formulate cinematic video editing as an energy minimization problem over shot selection, where cinematic constraints determine shot choices, transitions, and continuity. EditIQ~ synthesizes an aesthetically and visually compelling representation of the original narrative while maintaining cinematic coherence and a smooth viewing experience.Efficacy of EditIQ~ against competing baselines is demonstrated via a psychophysical study involving twenty participants on the \textit{BBC Old School} dataset plus eleven theatre performance videos.Video samples from \ourmethod~ can be found at \url{https://editiq-ave.github.io/}.<br/