Implicit Leadership Theories and Traits: A Qualitative Study of Managers in a Medical Sciences University
Bibliographic record
Abstract
Saeed Amini, Meghdad Rahati, Shabnam Atash-Parvar, Mahdieh Panahi, Marzieh Shahpari Department of Health and Management Sciences, Khomein University of Medical Sciences, Khomein, IranCorrespondence: Saeed Amini, Email sa_536@yahoo.comBackground: Understanding how healthcare professionals conceptualize leadership is vital for developing effective leadership development programs in medical universities. However, little is known about Implicit Leadership Theories (ILTs) within the unique context of Iranian medical universities, where education and healthcare service provision intersect.Aim: This study explored implicit leadership expectations among faculty and staff in a medical university setting, emphasizing the novelty of examining ILTs in Iran’s academic health system and its potential relevance for similar institutions globally.Methods: A qualitative phenomenological approach was employed to investigate ILTs among faculty and managers at Khomein University of Medical Sciences, a regional institution integrating healthcare services with clinical education. Fifteen participants were purposively sampled and interviewed using a semi-structured guide, drawn from twenty invited individuals. Data were analyzed using Colaizzi’s method with support from MAXQDA. Ethical approval was obtained, and informed consent was secured from all participants.Results: Four domains of ILTs were identified: (1) ethical and personal traits such as honesty, justice, and humility; (2) managerial and organizational competencies and capabilities, including planning, decision-making, and accountability; (3) communication and relational behaviors emphasizing respect, empathy, and participation; and (4) structural and cultural conditions shaping leader effectiveness.Conclusion: Findings highlight the need to align leadership behaviors with follower expectations to foster trust, strengthen leadership development, and enhance institutional performance. Situating ILTs in a non-Western academic health setting expands the global literature on leadership and followership, offering insights for medical universities in Iran and comparable systems internationally.Keywords: implicit leadership theories, medical university, qualitative research, healthcare leadership
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".