Saudi Women's Views on Healthcare Leadership in the Era of Saudi 2030 Health Transformation.

Introduction: Saudi Vision 2030 emphasizes women's empowerment and their increased participation in the workforce, particularly in healthcare. This study explores perceptions of Saudi women healthcare practitioners in leadership roles across various healthcare settings. Methods: A national cross-sectional online survey was conducted, guided by the Leadership Effectiveness Model. It assessed the perceptions of Saudi women health practitioners in leadership positions within the country's healthcare context. Survey questions were adapted from validated surveys. Women classified as "Consultants" by the Saudi Commission for Health Specialties were invited to participate. Descriptive statistics and content analysis were used for analysis. A total of 119 Saudi women consultants participated. Discussion: Most were physicians (85%) in the Western region (46%) and reported being in leadership roles. Leadership positively impacted their career growth but negatively affected leisure activities. Career progression challenges included further studies (35%) and work-life balance (31%). Leadership commitment to supporting women was seen as crucial (63%). Analysis of responses to Vision 2030 yielded themes like "advancement", "opportunities", and "empowerment". Saudi Vision 2030, combined with evolving organizational cultures and policies, is creating opportunities for women to excel in leadership roles. Conclusion: National strategies, combined with workplace norm changes and supportive policies, can foster greater representation of qualified women in elite healthcare leadership positions.

The Saudi Ministries Twitter communication strategies during the COVID-19 pandemic: A qualitative content analysis study.

Objectives: To understand government communication strategies during the COVID-19 pandemic by examining topics related to COVID-19 posted by Saudi governmental ministries on Twitter and situating our findings within existing health behavior theoretical frameworks. Study design: Retrospective content analysis of COVID-19 related tweets. Methods: On November 7th, 2020, we extracted relevant tweets posted by five Saudi governmental ministries. After we extracted the data, we developed and applied a coding schema. Results: A total of 3,950 tweets were included in our dataset. Topics fell into two groups: disease-related (49.2%) and non-disease related (50.8%). The disease-related group included seven categories: awareness (18.5%), symptom (0.6%), prevention (7.7%), disease transmission (1.9%), treatment (0.3%), testing (3.4%), and reports (16.7%). The non-disease related group included eight categories: lockdown (5.9%), online learning (12.8%), digital platforms (4.3%), empowerment (12.0%), accountability (1.1%), non-disease reports (2.1%), local and international news (10.8%), and general statements (1.9%). Based on the correlation analysis, we found that the top positively correlated categories were: "testing" and "digital platforms" (r = 0.4157), "awareness" and "prevention" (r = 0.3088), "prevention" and "disease transmission" (r = 0.3025), "awareness" and "disease transmission" (r = 0.1685), "symptom" and "testing" (r = 0.1081), "awareness" and "symptom" (r = 0.0812), "symptom" and "digital platforms" (r = 0.0645), and "disease transmission" and "digital platforms" (r = 0.0450), p-values < 0.01. Several health behavior theoretical constructs were linked to our findings. Conclusions: Integrating behavioral theories in the development of health risk communication should be taken seriously by government communication specialists who manage social media accounts, as these theories help underlining determinants of people's behaviors.

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia.

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: "Sehha", "Mawid", "Sehhaty", "Tetamman", "Tawakkalna", and "Tabaud". We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were "Tawakkalna" followed by "Tabaud", and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps' services and user experience, especially during health crises.