Assessing Leadership Behaviour Using Modified Multifactor Leadership Questionnaire (MLQ) among Healthcare Managers Working at Government Health Agencies in Bangladesh
Bibliographic record
Abstract
Background: Assessing leadership behaviour has become more important than ever in recent years to enhance the managerial and leadership qualities of health managers to improve the capacity of healthcare delivery to the people of the country. Objective: To assess the leadership behaviour of healthcare managers working at two largest government agencies in health sector of Bangladesh. Materials & Methods: This cross-sectional study was conducted among 154 medical doctors working in different management positions, between January and December of 2023. Data were collected by face-to-face interview using modified Multifactor Leadership Questionnaire (MLQ). Four leadership styles (autocratic, democratic, transformational and laissez-faire) were assessed. 15 factors were taken into consideration; each factor had 3 associated questions. Using factor analysis, leadership behaviour was classified as high, moderate and low; low value was 0–4, while medium and high values were 5–8 and 9–12 respectively. Results: The mean age of the respondents was (43.25±8.387) years; 35.7% belonged to 41–50 years age group. 74.7% were male and 25.3% were female. Most of our study participants exhibited moderate levels of autocratic (66.2%) and laissez-faire (50.2%) leadership behaviour in their managerial roles. In contrast, they displayed high levels of democratic (96.8%) and transformational (93.5%) leadership behaviour. Through factor analysis, autocratic leadership behaviour was found at moderate levels among healthcare managers, while high levels of democratic and transformational and low levels of laissez-faire leadership behaviour were adopted by them. Conclusion: Healthcare managers must have clear understanding of various leadership styles to lead their organization towards highest level of quality health service. The results of this study may lead to development of necessary leadership education and training programmes for healthcare professionals of our country. BJME, Volume-16, Issue-02, July 2025: 115-121
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".