Structural distress: experiences of moral distress related to structural stigma during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has taken a significant toll on the health of structurally vulnerable patient populations as well as healthcare workers. The concepts of structural stigma and moral distress are important and interrelated, yet rarely explored or researched in medical education. Structural stigma refers to how discrimination towards certain groups is enacted through policy and practice. Moral distress describes the tension and conflict that health workers experience when they are unable to fulfil their duties due to circumstances outside of their control. In this study, the authors explored how resident physicians perceive moral distress in relation to structural stigma. An improved understanding of such experiences may provide insights into how to prepare future physicians to improve health equity. METHODS: Utilizing constructivist grounded theory methodology, 22 participants from across Canada including 17 resident physicians from diverse specialties and 5 faculty members were recruited for semi-structured interviews from April-June 2020. Data were analyzed using constant comparative analysis. RESULTS: Results describe a distinctive form of moral distress called structural distress, which centers upon the experience of powerlessness leading resident physicians to go above and beyond the call of duty, potentially worsening their psychological well-being. Faculty play a buffering role in mitigating the impact of structural distress by role modeling vulnerability and involving residents in policy decisions. CONCLUSION: These findings provide unique insights into teaching and learning about the care of structurally vulnerable populations and faculty's role related to resident advocacy and decision-making. The concept of structural distress may provide the foundation for future research into the intersection between resident well-being and training related to health equity.
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 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 | 0.002 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".