Barriers to supportive care during the Ebola virus disease outbreak in West Africa: Results of a qualitative study
Bibliographic record
Abstract
BACKGROUND: During the 2013-2016 West Africa Ebola outbreak, supportive care was the only non-experimental treatment option for patients with Ebola virus disease (EVD). However, providing care that would otherwise be routine for most clinical settings in the context of a highly contagious and lethal pathogen is much more challenging. The objective of this study was to document and deepen understanding of barriers to provision of supportive care in Ebola treatment units (ETUs) as perceived by those involved in care delivery during the outbreak. METHODS: This qualitative study consisted of 29 in-depth semi-structured interviews with stakeholders (decision-makers, physicians, nurses) involved in patient care delivery during the outbreak. Analysis consisted of interview debriefing and team-based transcript coding in NVivo10 software using thematic analysis. FINDINGS: Participants emphasized three interconnected barriers to providing high-quality supportive care during the outbreak: 1) lack of material and human resources in ETUs; 2) ETU organizational structure limiting the provision of supportive clinical care; and 3) delayed and poorly coordinated policies limiting the effectiveness of global and national responses. Participants also noted the ethical complexities of defining and enacting best clinical practices in low-income countries. They noted tension between, on one hand, scaling up minimal care and investing in clinical care preparedness to a level sustainable in West Africa and, on the other, providing a higher level of supportive care, which in low-resource health systems would require important investments. CONCLUSION: Our findings identified potentially modifiable barriers to the delivery of supportive care to patients with EVD in West Africa. Addressing these in the inter-outbreak period will be useful to improve patient care and outcomes during inevitable future outbreaks. Promoting community trust and engagement through long-term capacity building of the healthcare workforce and infrastructure would increase both health system resilience and ability to handle other outbreaks of emerging diseases.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".