A Phenomenological Study of Postgraduate Medical Trainees’ Incidental Learning Experiences and Psychological Well-Being During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, postgraduate medical trainees contributed significantly to the healthcare workforce, as multiple vulnerabilities in the healthcare system and medical training were expounded. The burden of training, learning, and working at this time introduced unique psychological and emotional stressors within a context of generalized volatility and radically different ways to work and learn. This study explored postgraduate trainees' experiences with coping, managing, and learning during the COVID-19 pandemic. METHODOLOGY: = 8) of postgraduate trainees in Newfoundland and Labrador, Canada, between May and October 2022. Five researchers performed inductive and deductive thematic analysis to develop a coding structure and identify common themes. RESULTS: The COVID-19 pandemic prompted the use of restrictive public health measures and an unprecedented shift from in-person to virtual learning. This affected trainees' exposure to normalized learning experiences, training rotations, and opportunities to learn from peers and staff. Certainly, trainees reported that virtual learning improved their educational experiences in unique ways, increased engagement and attendance, and enabled regular meetings and learning when in-person options were unavailable. Trainees also reported enhanced self-directed learning skills, greater ownership of and leadership in their education, and increased confidence and experience with virtual care. Some also reported a perceived increase in elements of emotional intelligence (e.g., self-awareness, empathy, and compassion). CONCLUSIONS: Trainees reported a variety of incidental learning experiences from working and training during COVID-19. Although some experiences were challenging, there was a perception that such experiences led to new learnings that were beneficial to one's professional development and future career, as well as implications for future training provided to trainees. While there was a reported shift in the culture surrounding postgraduate trainees' health and safety, respondents also described the need for additional support for postgraduate trainees' well-being during a pandemic.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".