Saudi Arabian Women in Medical Education: A Mixed Method Exploration of Emergent Digital Leadership
Bibliographic record
Abstract
Background: Saudi Arabian women’s leadership in medical education is evolving. As in Western contexts, the number of women in formal leadership positions in Saudi medical education is increasing. However, given the unique cultural context, Saudi women health professionals may have less influence in their organizations than their counterparts in the West. Novel digital approaches may offer women a form of leadership by which their influence might be increased. Using the Mededlam.com “LAM” digital initiative, an online digital community dedicated to helping medical professionals in their leadership, teaching, and research roles by disseminating digital content that tackle different topics in medical education, this study explores the emergence of women’s leadership through their participation in an online professional community. Objectives: 1) Establish a common understanding of leadership in a digital context amongst women who are members of the LAM community. 2) Investigate why they have turned to the LAM digital initiative to exercise influence in their profession; and, 3) Explore women’s opinions regarding their online interactions on LAM, including how those opinions have influenced their leadership identities and professional influence/development. Methods: To explore the emergence of women’s leadership in a digital context, a sequential explanatory mixed method approach was adopted. In phase one, a questionnaire was developed based on literature review findings. The questionnaire was disseminated through the LAM website and affiliated social media pages. Seventy-nine women took part in the quantitative phase and data were analyzed using descriptive statistics. Women who expressed willingness to participate in phase two were invited through email to take part. In phase two, 15 semi-structured interviews were conducted. Qualitative data were analysed using framework analysis. Results: In the first phase, respondents agreed or strongly agreed with definitions provided for leadership in a digital context. Respondents were reluctant to define their use of digital tools as a bid for influence in medical education. In the second phase, qualitative data revealed that women perceived digital tools as novel method of influence for women. However, they identified several issues that deter them from utilizing such tools, including fear of appearing unprofessional and a lack of knowledge on how to influence people online. Conclusion: The potential of digital media as a tool to exercise influence and leadership for women in their profession is promising. For Saudi Arabian women health professionals in medical education, media such as LAM can provide a complementary forum to their real-world leadership and can extend their influence beyond their work environments.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".