Healthcare systems are increasingly required to utilize effective approaches, apply evidence-based practice, and consequently sustain successful strategic management. Document analysis provides insights into the effective management tools applied by agencies to respond to crises. This article provides a practical exploration of how the Saudi health authority applied effective measures to eventually reduce the administrative and clinical consequences while managing the COVID-19 pandemic. The conceptual descriptive framework was based on health policy triangle of Walt and Gilson. Official reports and supporting documents issued by the Saudi government toward COVID-19 were operationally analyzed. Moreover, five healthcare professional experts were invited in a semistructured interview to assess the strategic steps that have been utilized to minimize the health risk by conducting a healthcare risk analysis. Various documents showed that two major entities were responsible for managing regulations and medications of COVID-19 in addition to six other entities that were partially involved. Although each entity was approved to work independently, their efforts were cohesively associated with each other. Most documents were well-applied on personal, social, organizational, and national strata. However, it is unclear how lessons identified became affirmative, while the collaboration remains vague, especially under the emergence of a new entity such as the Public Health Authority. Healthcare professional experts also positively supported the effectiveness of such policies to confront COVID-19 through the following three domains: health guidelines, utilizing simulation (telehealth/telecommunication) services, and ensuring continuity of services.

Quality of life (QoL) is considered one of the measures of health outcomes. Limited research studies have assessed family caregivers’ QoL, especially among patients diagnosed with chronic disease. This study measures the QoL of caregivers who guardian patients diagnosed with cardiovascular disease, diabetes, cancer, and/or other diseases during the COVID-19 pandemic. Participants were primary caregivers who were supporting, in the last six months, individuals diagnosed with one of the previously mentioned chronic diseases. This included caregivers of patients admitted to a tertiary hospital from January 2021 to July of the same year (n = 1081); all participants completed the World Health Organization Quality of Life Assessment tool (WHOQOL-BREF) questionnaire. Caregivers of patients with cancer reported the highest mean level of QoL, followed by diabetes, cardiovascular diseases, then other different diseases (M = 3.80; M = 3.38; M = 3.37; and M = 2.51, respectively). A chi-square test of independence was performed to examine the relationship between the QoL of the four groups and their behaviors (i.e., caregivers’ psychological onuses and physical actions/reactions). The relation between these variables was significant, X2 (3, n = 1081) = 8.9, p = 0.001. The Kruskal–Wallis test indicated significant differences among the four groups (p ≤ 0.001). While the overall results of the QoL level of participants were low, a major recommendation of this study was to incorporate a QoL assessment to caregivers of chronically ill patients. Regular psychological and physical health check-ups of caregivers should be mandated in the healthcare system. Research studies should consider investigating and identifying the factors affecting health outcomes and positive developments which have a great impact on the wellbeing of both caregivers and patients on personal, organizational, and national levels.

Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.