Lorsque les vécus d’oppression se propagent des patient.es aux chercheur.es : comment intégrer les données expérientielles à la recherche en santé mondiale ?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it