Under consent: participation of people with HIV in an Ebola vaccine trial in Canada
Bibliographic record
Abstract
BACKGROUND: Little is known about volunteers from Northern research settings who participate in vaccine trials of highly infectious diseases with no approved treatments. This article explores the motivations of HIV immunocompromised study participants in Canada who volunteered in a Phase II clinical trial that evaluated the safety and immunogenicity of an Ebola vaccine candidate. METHODS: Observation at the clinical study site and semi-structured interviews employing situational and discursive analysis were conducted with clinical trial participants and staff over one year. Interviews were recorded, transcribed and analysed using critical qualitative interpretivist thematic analytical techniques. Patterns were identified, clustered and sorted to generate distinct and comprehensive themes. We then reassembled events and contexts from the study participants' stories to develop two ideal portraits based on "composite characters" based on study participants features. These provide ethnographically rich details of participants' meaningful social worlds while protecting individual identities. RESULTS: Ten of the 14 clinical trial participants, and 3 study staff were interviewed. Participant demographics and socio-economic profiles expressed limited contextual diversity. Half were men who have sex with men, half were former injection drug users experiencing homelessness, one was female, none were racialized minorities and there were no people from HIV endemic countries. Fully 90% had previous involvement in other clinical studies. Their stories point to particular socio-economic situations that motivated their participation as clinical labor through trial participation. CONCLUSIONS: Our findings support Fisher's argument of "structural coercion" in clinical trial recruitment of vulnerable individuals experiencing precarious living conditions. Clinical trials should provide more detail of the structural socio-economic conditions and healthcare needs which lie "under consent" of study participants. Going well beyond an overly convenient narrative of altruism, ethical deliberation frameworks need to sufficiently address the structural conditions of clinical trials. We offer concrete possibilities for this and acknowledge that further research and clinical data should be made available underlying study participant contexts with regards to recruitment and participation in resource poor settings, in both the South and the North.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.285 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".