Troubling Objectivity: The Promises and Pitfalls of Training Haitian Clinicians in Qualitative Research Methods
Bibliographic record
Abstract
Building research capacity is a central component of many contemporary global health programs and partnerships. While medical anthropologists have been conducting qualitative research in resource-poor settings for decades, they are increasingly called on to train "local" clinicians, researchers, and students in qualitative research methods. In this article, I describe the process of teaching introductory courses in qualitative research methods to Haitian clinicians, hospital staff, and medical students, who rarely encounter qualitative research in their training or practice. These trainings allow participants to identify and begin to address challenges related to health services delivery, quality of care, and provider-patient relations. However, they also run the risk of perpetuating colonial legacies of objectification and reinforcing hierarchies of knowledge and knowledge production. As these trainings increase in number and scope, they offer the opportunity to reflect critically on new forms of transnational interventions that aim to reduce health disparities.
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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 | 0.830 | 0.769 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.024 | 0.163 |
| Scholarly communication | 0.027 | 0.050 |
| Open science | 0.011 | 0.037 |
| Research integrity | 0.017 | 0.039 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".