Reflecting on experiences of resident redeployment during the COVID‐19 pandemic: Implications for leadership and theory beyond the crisis
Bibliographic record
Abstract
INTRODUCTION: This study explored medical residents' experiences of redeployment during the COVID-19 pandemic. With the benefit of time and reflection, this study went beyond an 'educational deficit' perspective on redeployment and examined these experiences to better understand enduring tensions in medical education, prepare leaders for ongoing tensions and future crises, and to inform professional identity formation theory. METHODS: This was a qualitative, interpretive study informed by professional identity formation concepts related to work-identity integrity threats. Between April and November 2023, 15 residents from seven specialties at a large urban university in Canada were interviewed about redeployment processes and experiences. An abductive analysis approach was used to examine how residents made meaning of their profession, specialty and workplace in light of these experiences. RESULTS: The meaning participants made of redeployment processes depended on their interpretations of fairness, alignments with their perceived identity as a physician, and sense of usefulness during redeployment experiences. While participants noted a lack of socialisation and connection within their specialty as potentially disruptive to professional identity formation, broader sociopolitical dynamics (e.g. anti-vaccine movements) and local microcontexts (e.g. appreciative clinical teams) mattered most in their reflections. Experiences of redeployment elicited reflections on historical relationships between specialties. Some of those reflections were specific to the pandemic context, while others prompted broader reconsiderations of trends towards hyperspecialisation within the profession. DISCUSSION: These results provide insight into how future crises might be best approached, but also how wellness and resilience might be supported in non-crisis situations. This analysis also suggests a potential unfreezing of long-standing interspecialty tensions. The endurance of these shifting dynamics is worth exploring, particularly in light of policy imperatives towards more flexible and responsive systems. Theoretically, this analysis invites considerations of professional identity formation to better account for broader sociopolitical dynamics and the local dynamics of workplaces.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".