Moral Distress and Resilience Associated with Cancer Care Priority Setting in a Resource-Limited Context
Bibliographic record
Abstract
BACKGROUND: Moral distress and burnout are highly prevalent among oncology clinicians. Research is needed to better understand how resource constraints and systemic inequalities contribute to moral distress in order to develop effective mitigation strategies. Oncology providers in low- and middle-income countries are well positioned to provide insight into the moral experience of cancer care priority setting and expertise to guide solutions. METHODS: Semistructured interviews were conducted with a purposive sample of 22 oncology physicians, nurses, program leaders, and clinical advisors at a cancer center in Rwanda. Interviews were recorded, transcribed verbatim, and analyzed using the framework method. RESULTS: Participants identified sources of moral distress at three levels of engagement with resource prioritization: witnessing program-level resource constraints drive cancer disparities, implementing priority setting decisions into care of individual patients, and communicating with patients directly about resource prioritization implications. They recommended individual and organizational-level interventions to foster resilience, such as communication skills training and mental health support for clinicians, interdisciplinary team building, fair procedures for priority setting, and collective advocacy for resource expansion and equity. CONCLUSION: This study adds to the current literature an in-depth examination of the impact of resource constraints and inequities on clinicians in a low-resource setting. Effective interventions are urgently needed to address moral distress, reduce clinician burnout, and promote well-being among a critical but strained oncology workforce. Collective advocacy is concomitantly needed to address the structural forces that constrain resources unevenly and perpetuate disparities in cancer care and outcomes. IMPLICATIONS FOR PRACTICE: For many oncology clinicians worldwide, resource limitations constrain routine clinical practice and necessitate decisions about prioritizing cancer care. To the authors' knowledge, this study is the first in-depth analysis of how resource constraints and priority setting lead to moral distress among oncology clinicians in a low-resource setting. Effective individual and organizational interventions and collective advocacy for equity in cancer care are urgently needed to address moral distress and reduce clinician burnout among a strained global oncology workforce. Lessons from low-resource settings can be gleaned as high-income countries face growing needs to prioritize oncology resources.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".