Nurses' experiences of their decision‐making process when participating in clinical trials during the 2014–2016 West African Ebola crisis
Bibliographic record
Abstract
OBJECTIVE: Due to the absence of a licenced vaccine or drug for treatment of Ebola patients during the 2014-2016 West Africa outbreak, frontline nurses were at increased risk of exposure. Hence, they were prioritized to participate in clinical trials to receive experimental therapeutics. To our knowledge no study has explored the nurses' experiences of their decision-making process when volunteering in clinical trials using unproven agents, which is the purpose of this qualitative study. METHODS: This study, part of a larger Ebola study, thematically analyzed the interview data of nine nurses recruited from Sierra Leone, Guinea and Liberia; of which four joined a convalescent plasma trial and five a vaccine trial. RESULTS: In their decision-making process to partake in a clinical trial, nurses identified two distinct decision points: the initial commitment followed by the point of no return when they presented themselves to participate. Each of these decisions were influenced by risk versus benefits calculations, and contextual factors. CONCLUSION: Results showed the need for more health education and communication around the unproven agents in order for nurses to make informed decisions.
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 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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".