Emotional Intelligence and Leadership Styles Among Managers in Primary Healthcare Centers, Riyadh, Saudi Arabia
Bibliographic record
Abstract
Aeshah Abdullah Alasmari,1 Raseel Abdulaziz Awad,2 Abdulmajeed Mohamed Alshowair,3 Saad M Albattal,4 Abdulmajeed Homaidan AlMutairi,4 Amro Abdel-Azeem,5 Mostafa Kofi4 1Family Medicine Department, Riyadh First Health Cluster Ministry of Health, Riyadh, Saudi Arabia; 2Al-Murabba PHC, Riyadh First Health Cluster Ministry of Health, Riyadh, Saudi Arabia; 3VP Community Health Excellence, Riyadh First Health Cluster Ministry of Health, Riyadh, Saudi Arabia; 4Family Medicine Department, Prince Sultan Military Medical City, Ministry of Defense Health Services, Riyadh, Saudi Arabia; 5Population Health Management and Research, Riyadh First Health Cluster Ministry of Health, Riyadh, Saudi ArabiaCorrespondence: Mostafa Kofi, Family Medicine Department, Prince Sultan Military Medical City, Ministry of Defense Health Services, Riyadh, Saudi Arabia, Tel +966501436859, Email Moustafafouad@yahoo.com Amro Abdel-Azeem, Population Health Management and Research, Riyadh First Health Cluster Ministry of Health, Riyadh, Saudi Arabia, Tel +966547135224, Email dr_amro_osh@hotmail.comPurpose: Effective leadership is one of the most important factors contributing to an organization’s effectiveness and success. The objective of this study is to identify the leadership styles of primary healthcare managers and explore associated sociodemographic factors.Methods: A cross-sectional study was conducted at Prince Sultan Military Medical City (PSMMC) in Riyadh, Saudi Arabia, among primary healthcare managers in different aspects of 6 primary healthcare centers (PHC). Data were collected using a self-assessment questionnaire composed of two main sections: sociodemographic characteristics of the participants and the emotional intelligence questionnaire to assess the various competencies of emotional intelligence of leadership style.Results: A total of 50 primary healthcare managers were included in the study. Approximately half of them (52%) were aged between 35 and 44 years. The highest applied statement was “I know when I am happy (4.42± 0.95)”, whereas the lowest applied statement was “I rarely worry about work or life in general (3.20± 1.26)”. Self-awareness was considered a strength in most of the participants (78%), while, managing emotions needs attention in 52% of them. Intrinsic motivating, empathy, and social skill were considered strengths in most of the participants (70%, 74%, and 68%, respectively). Participants aged (25– 34 years) were more likely than others to need attention in the component of “intrinsic motivating” (31.6%), p=0.053. Single participants were more likely than married participants to need attention in the component of “intrinsic motivating” (70% versus 18.4%). P=0.006. Nurses, pharmacists and radiologists were more likely to need attention in the component of “self-awareness” than doctors and directors, p = 0.041.Conclusion: This study highlights the significance of emotional intelligence components, such as self-awareness, empathy, and social skills, as strengths among primary healthcare managers in Riyadh, Saudi Arabia. The findings emphasize that enhancing emotional intelligence is essential for fostering effective leadership within primary healthcare sitting.Keywords: leadership styles, emotional intelligence questionnaire, primary healthcare centers, healthcare management, transformational leadership, vision 2030
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".