Lorsque les vécus d’oppression se propagent des patient.es aux chercheur.es : comment intégrer les données expérientielles à la recherche en santé mondiale ?
Bibliographic record
Abstract
INTRODUCTION: Global health researchers tend to focus on the social and structural determinants of health and offer solutions for the "decolonization of public health" by addressing these determinants. These solutions address the root causes of social inequalities in health, but too often ignore the affective and intersubjective dimensions that underlie the complex human relationships in global health. METHODS: In this article, we focus on experiential data through the concept of geo-corpo-political knowledge (Tlostanova & Mignolo, 2009). We explore the emotional experiences we can have as researchers engaging in authentic dialogue in research sites. We draw on collaborative research with Doctors of the World in Montreal, Quebec, focusing on barriers to health care for undocumented migrants to inform our analysis. RESULTS: In qualitative interviews with caregivers working with this population, we identified paradoxes, areas of silence, and difficulties in verbalizing their lived and emotional experiences. DISCUSSION: In this paper we go beyond what can be put into words. We aim to explore our feelings as researchers to offer a systemic understanding of the oppression experienced by patients in their interactions with healthcare institutions. We argue that to reach the most marginalized populations and better understand their experiences, it is important to develop research methods that integrate the emotional world of researchers.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".