A phenomenological exploration of the impact of COVID‐19 on the medical education community
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has caused unprecedented stress to the medical education community, potentially worsening problems like burnout and work-life imbalance that its members have long been grappling with. However, the collective struggle sparked by the pandemic could generate the critical reflection necessary for transforming professional values and practices for the better. In this hermeneutic phenomenological study, we explore how the community is adapting-and even reconceptualising-their personal and professional roles amidst the COVID-19 crisis. METHOD: Between April and October 2020, we conducted 27 (17F, 10M) semi-structured interviews with medical trainees (8), physicians (8), graduate students (3) and PhD scientists (8) working in medical education in Canada, the United States and Switzerland. Data analysis involved a variety of strategies, including coding for van Manen's four lifeworld existentials, reflexive writing and multiple team meetings. RESULTS: Participants experienced grief related to the loss of long-established personal and professional structures and boundaries, relationships and plans for the future. However, experiences of grief were often conflicting. Some participants also experienced moments of relief, perceiving some losses as metaphorical permissions slips to slow down and focus on their well-being. In turn, many reflected on the opportunity they were being offered to re-imagine the nature of their work. DISCUSSION: Participants' experiences with grief, relief and opportunity resonate with Ratcliffe's account of grief as a process of relearning the world after a significant loss. The dismantling of prior life structures and possibilities incited in participants critical reflection on the nature of the medical education community's professional practices. Participants demonstrated their desire for more flexibility and autonomy in the workplace and a re-adjustment of the values and expectations inherent to their profession. On both individual and systems levels, the community must ensure that long-standing calls for wellness and work-life integration are realised-and persist-after the pandemic is over.
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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.040 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".