Hidden Tales of Ebola: Airing the Forgotten Voices of Ugandan “Ebola Nurses”
Bibliographic record
Abstract
INTRODUCTION: According to the Centers for Disease Control and Prevention, Ebola has affected the lives of thousands, including health care workers. With few studies describing the experience of nurses who survived Ebola, the study aimed to describe Ugandan nurses' experiences. METHOD: Using a phenomenological design, in-depth interviews were conducted among five Ugandan nurses who contracted Ebola and survived. RESULT: Thematic analysis revealed themes of expectations of dying, hopelessness, loneliness, and betrayal by family, community, and the health system. DISCUSSION: Results support the need for policies targeting holistic practice protocols to protect all health care professionals during future outbreaks. Last, nursing survivors should have access to government-guaranteed support programs, including free health care and financial stipends. These results and recommendations transcend to the current reality of living with COVID-19 (coronavirus disease 2019). Efficient practice protocols could protect all rights and privileges and contribute to access to treatment and stigma removal.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 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".