Long-Term Experiences of Health Care Providers Using Iris Scanning as an Identification Tool in a Vaccine Trial in the Democratic Republic of the Congo: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Iris scanning has increasingly been used for biometric identification over the past decade, with continuous advancements and expanding applications. To better understand the acceptability of this technology, we report the long-term experiences of health care providers and frontline worker participants with iris scanning as an identification tool in the EBL2007 Ebola vaccine trial conducted in the Democratic Republic of the Congo. OBJECTIVE: This study aims to document the long-term experiences of using iris scanning for identity verification throughout the vaccine trial. METHODS: Two years after the start of the EBL2007 vaccine trial (February to March 2022), 69 trial participants-including nurses, first aid workers, midwives, and community health workers-were interviewed through focus group discussions. Additionally, 13 in-depth individual interviews were conducted with physicians involved in the trial, iris scan operators, trial staff physicians, and trial participants who declined iris scanning. Qualitative content analysis was used to identify key themes. RESULTS: Initially, interviewees widely accepted the iris scan and viewed it as a distinctive tool for identifying participants in the EBL2007 vaccine trial. However, over time, perceptions became less favorable. Some participants expressed concerns that their vision had diminished shortly after using the tool and continued to decline until the end of the study. Others reported experiencing perceived vision loss long after the trial had concluded. However, no vision impairment was reported as an adverse event or assessed in the trial as being linked to the iris scan, which uses a previously certified safe infrared light for scanning. CONCLUSIONS: Our findings highlight the sustained acceptability and perceived high accuracy of the iris scan tool for uniquely identifying adult participants in a vaccine trial over time. Continued efforts to systematically disseminate and reinforce information about the function and safety of this technology are essential. Clearly presenting iris scanning as a safe procedure could help dispel misconceptions, concerns, and perceived risks among potential users in vaccine trials.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".