A Qualitative Exploration of Hong Kong Medical Educators’ Perspectives on Factors Influencing Their Resilience
Bibliographic record
Abstract
Introduction: Globally, alarming trends of psychological distress among physicians and medical students threaten patient care and professionalism. The resilience and well-being of medical educators have been recognised as key influences on learners. However, relevant research is limited, especially in Asian contexts. Using the National Academy of Medicine (NAM) model as a lens, this study explores what external and individual factors impact the resilience of Hong Kong (HK)-based medical educators. Methods: HK-based medical educators, who taught medical students and physicians, were recruited using purposive sampling. They participated in semi-structured online interviews from 06/2021 to 04/2022. Anonymous sociodemographic information was collected through an online survey, and video recordings were transcribed anonymously. Guided by the NAM model, a hybrid deductive and inductive thematic analysis was conducted. Results: (social support from family, friends, and colleagues, and a sense of purpose in their roles) were perceived as influencing their resilience to a similar extent, suggesting that both organisational support and individual connections can bolster medical educators' resilience. Discussion: This study, the first of its kind in Asia, examined the applicability and contextual suitability of the NAM model for use among HK-based medical educators. They perceived organisational and individual factors as complementary in influencing their resilience. Our findings highlighted the importance of considering both system- and individual-level aspects when designing strategies for promoting resilience in this population.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".