Pharmacogenomics Research Involving Racial Classification: Qualitative Research Findings on Researchers’ Views, Perceptions and Attitudes Towards Socioethical Responsibilities
Bibliographic record
Abstract
BACKGROUND: Racial classification of study samples has been a common practice since the early days of pharmacogenomics research. The use of race as an axis of stratification in the study of human genetic variation and population differences in drug response has come under intense scrutiny recently, particularly among policy makers, clinicians and researchers. However, there are no published empirical data on how genomics scientists perceive ethical concerns or view their own professional roles when they are confronted with this issue in their everyday practice or have to decide themselves whether racial classification should be a cornerstone of their research work. OBJECTIVES: To investigate the views and perceptions of researchers on the use of racial classification in pharmacogenomics research using a qualitative research methodology. METHODS: We interviewed genomics researchers about their perceptions on pharmacogenomics, race and science, population-based genomics research and the attendant implications for their professional duties. We sought out researchers who self-identified with many of the populations likely to be solicited for race-specific pharmacogenomics research. A thematic investigation of the semistructured interviews was undertaken using the qualitative data software program ATLAS.ti to extract and systematically analyze complex phenomena (e.g., professional viewpoints) embedded in the narratives from the interviews. RESULTS: The participants expressed the 'doubled-edged' nature of pharmacogenomics research involving racial classification while also having a cautiously optimistic view of race-based therapeutics. They believed that pharmacogenomics could improve health outcomes for racially defined populations in the context of health disparities. Sensitized to racism and potential abuses, they expressed concerns and need for precautionary measures over the sensitive nature of racially categorized research results. On the other hand, researchers perceived themselves as being responsible primarily for providing raw scientific data. CONCLUSION: Researchers engaged in genomics investigations appear to display a guarded and yet favorable perception on the utility of race in pharmacogenomics investigations. Interestingly, researchers remain sceptical of their own roles vis-à-vis ethics and delegating socioethical responsibilities to ethicists was seen as a way to remedy this shortfall instead of broadening the scope of self-governance in scientific practice to socioethical issues. While these data do not necessarily reflect views and attitudes of all scientists, future science policy questions on how best to integrate molecular genetics with race-based therapeutics and incorporation of socioethical reflection in daily practice of genomics research need to consider the perceptions of scientists and similar 'upstream control points' in the process of knowledge generation and dissemination.
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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 | 0.126 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".