“Is There Anything Else You Would Like to Add?”: The Ethics of (Not) Addressing Research Participants' Top Concerns in Public Health Emergency Health Research
Bibliographic record
Abstract
When conducting interviews or focus groups, researchers often end with a simple question; "Is there anything else you would like to add?" This article takes responses to this question provided by participants in a study of "West Africans' Perceptions of Ebola research" as its point of departure. A number of participants in that study accepted the invitation to add on to their interview at its end with details of suffering from the sequelae of Ebola in their communities, and criticisms of state social abandonment. Some explicitly asked the researcher to ensure the suffering of Ebola survivors would be recognized at the international level. These closing words exceeded the objectives of the study within which they emerged. This was a study focused on lived experiences and decision-making to participate in Ebola research during or after the 2013-16 West Africa Ebola outbreak. The study aimed to inform the ethical conduct of research in future public health emergencies. What to do, then, in the face of these participants' entreaties to the interviewer for action to address Ebola survivors' suffering and social abandonment? Can and should the public health emergency or qualitative researcher better anticipate such requests? Where participants' expressed concerns and hopes for the impact of a study exceed its intended scope and the researchers' original intentions, what is at stake ethically in how we respond to those entreaties as researchers? This paper offers reflections on these questions. In doing so, our intention is to open up a space for further consideration and debate on the ethics of how researchers respond to unanticipated requests made to them in the course of research projects, to leverage their power and privilege to advance local priorities.
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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 | 0.499 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.158 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.025 | 0.037 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".