Doing our best and doing no harm: A focused ethnography of staff moral experiences of providing palliative care at a Médecins Sans Frontières pediatric hospital in Cox’s Bazar, Bangladesh
Bibliographic record
Abstract
INTRODUCTION: The Médecins Sans Frontières (MSF) Goyalmara Hospital in Cox's Bazar, Bangladesh is a referral centre offering the highest level of care available in the Rohingya camps for pediatrics and neonatology. Efforts are underway to integrate pediatric palliative care due to high mortality and medical complexity of patients, yet little is known about the experiences of staff delivering palliative and end-of-life care. The purpose of this study was to understand the moral experiences of MSF staff to inform program planning and implementation. METHODS: This focused ethnography was conducted between March-August 2021 at Goyalmara Hospital. Data collection involved participant-observation, individual interviews (22), focus group discussions (5), and analysis of documents including MSF clinical guidelines, admission and referral criteria, reports, and training materials. Data analysis followed a modified version of the Qualitative Analysis Guide of Leuven and data were coded using NVivo software. RESULTS: The prevailing understanding of pediatric palliative care among national and international staff was care that prioritized comfort for infants and children who were not expected to survive. Staff's views were informed by their sense of obligation to do no harm, to do their best on behalf of their patients, and religious beliefs about God's role in determining the child's outcome. The authority of doctors, international staff, as well as protocols and guidelines shaped palliative care decision-making. Staff saw clinical guidelines as valuable resources that supported a consistent approach to care over time, while others were concerned that palliative care guidelines were rigidly applied. CONCLUSION: When integrating palliative care into humanitarian programs, it is important to emphasize the active role of palliative care in reducing suffering. Advocacy for access to the highest level of care possible should continue alongside palliative care integration. While palliative care guidelines are valuable, it is essential to encourage open discussion of staff concerns and adapt care plans based on the family's needs and preferences.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".