Exploring interventions for fostering resilience among medical educators
Bibliographic record
Abstract
INTRODUCTION: Resilience is essential for medical educators to fulfil their responsibilities. While research often focuses on individual well-being programs, less is known about how resilience is shaped by systemic and institutional interventions, particularly across diverse cultural contexts. This study explores interventions perceived by Hong Kong (HK) medical educators as fostering their resilience. METHODS: an online survey. Twenty medical educators from two HK medical schools participated in video-recorded, semi-structured online interviews. Transcripts were anonymized and an abductive reflective thematic analysis was conducted. Researchers iteratively engaged with Bronfenbrenner's Process-Person-Context-Time (PPCT) model to deepen interpretation. RESULTS: Nine interrelated themes were identified across four PPCT domains. Resilience was shaped by personal reflection and self-care (Person); supportive relationships and communication (Process); institutional conditions (Context); and changes across life stages plus external events (Time). Process- and Context-level interventions were perceived as essential for fostering medical educators' resilience. DISCUSSION/CONCLUSION: The findings underscore the importance of designing resilience interventions that address relational, institutional, systemic and cultural dimensions. Key areas include professional recognition, communication, resource allocation, and psychologically safe environments. Understanding culturally- and contextually-specific experiences of resilience may assist in crafting fit-for-purpose resilience interventions for educators in multicultural environments.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".