Browsing by Author "Alghamdi, Zeyad"

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Mental Health on Social Media: AI-Driven Detection and Response
(Arizona State University, 2025) Alghamdi, Zeyad; Liu, Huan
Mental health issues are increasingly prevalent, with stress playing a critical role in the development of severe mental and physical health conditions. Early detection and effective intervention are essential for mitigating these challenges. In an increasingly digital world, social media serves as a valuable repository of large-scale data on how individuals vent and express stress. This data source captures two critical dimensions or perspectives: the individual and the social. The individual dimension is revealed through direct expressions of stress in users’ posts, where emotional states and linguistic patterns provide important indicators. In a synergistic manner, the social dimension is discerned from the reactions of others, offering contextual cues that reflect the broader environment’s influence on the user’s mental state. My dissertation builds on this dual perspective by integrating social science and psychological theories to inform a methodologies,that strengthens AI’s capacity to recognize stress-related cues and also to engage with mental health discourse in a refined and contextaware manner. To achieve this, I propose three innovative detection strategies that capture the individual and social dimensions. The first strategy focuses on analyzing the finegrained linguistic and emotional features to identify stress within individual posts, directly addressing the individual perspective. The second strategy extends this analysis by examining the broader contextual nuances embedded in these posts, thereby deepening the understanding of individual stress expressions. The third strategy shifts attention to the social perspective by incorporating emotional cues from community responses as auxiliary signals to enhance the stress classification. Finally, drawing on the insights from these works, I established a data-supported refinement process that improves AI’s ability to produce more supportive responses that are both contextually aware and socially attuned. This research exemplifies how interdisciplinary innovation can redefine AI’s role in addressing complex challenges in mental health.
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Nudges: A Behavioural Approach for Mitigating Missed Appointments at a Private Dental Clinic in Saudi Arabia
(Saudi Digital Library, 2023-12-01) Alghamdi, Zeyad; Davies, Julie
Missed appointments continue to bedevil private healthcare providers globally. It implies failure by a patient to arrive for an appointment or to cancel an appointment in advance. Clinics management plays an important role in mitigating no-shows among their patients. Some interventions have been implemented, such as sending reminder messages to patients about their upcoming appointments and imposing sanctions against those who fail to attend. However, the interventions have yet to mitigate the challenge completely. As a result, it is essential to explore alternative interventions with behavioural economics approaches considered more effective. Nudge theory is a behavioural theory, which seeks to influence the attainment of desired decisions among various populations, in this context, patients with clinical appointments. Subsequently, the review sought to assess the effectiveness of nudges in mitigating missed appointments. Methodology The study adopted a narrative literature review methodology to examine the use of nudges in mitigating missed appointments within Nayel Clinics (NC), a private dental health facility in Saudi Arabia. The adoption of nudges is analysed based on its influence on behaviour and appointment attendance. Findings The review concluded that missed appointments pose significant financial losses to clinics. However, the application of behavioural nudges was found to evoke more profound thoughts and analysis among patients leading to desirable decisions. The effective practices identified and recommended for applying nudges in NC involve developing an exclusive loyalty programme, creating a feedback loop for patients, and digitising the appointment confirmation process. These recommendations are suggested to encourage patients to attend clinical appointments or to make timely cancellations. Research limitations The study adopted a narrative literature review rather than a meta-analysis to explore interventions for missed appointments. Consequently, the findings may not have yielded precise recommendations. Additionally, the methodological quality of the reviewed studies varied, which may have influenced the outcomes. Future studies should focus on generating empirical evidence to validate the findings presented in the current review. Practical implications No-shows negatively impact healthcare systems globally by decreasing continuity of care and clinic capacity. Healthcare providers can implement recommendations from this review to increase appointment attendance. Keywords Missed appointments, No-show, Nudge theory, Nudge, Kingdom of Saudi Arabia, patient, behavioural economics, hospital, management. Article classification Research paper, A critical evaluation of nudge theory.
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