Production of the Global Health Doctor: Discourses on International Medical Electives
Bibliographic record
Abstract
This article attempts to interrupt dominant narratives in the literature about international service-learning (ISL) in the field of medicine by critically deconstructing discourse related to a common model used to teach global health in undergraduate medical education: the international medical elective (IME). Based on a study conducted in 2012, the results have not been previously published. Using a Foucauldian discourse analysis, the study interrogated the underlying assumptions behind the nature of “service” being rendered by conveying the imagery, language, and consequences of the dominant discourses used in journal articles indexed on MEDLINE between 2000 and 2011. The analysis revealed an IMEs literature steeped in problematic discursive (re)productions of colonial constructs and imagined geographies, primarily through two dominant discourses designated as “disease and brokenness” and “romanticizing poverty.” These discourses both justify and reinforce privileged subject positions for students engaged in these ISL experiences, while inadequately considering structures and systems that perpetuate marginalization and health inequities. Such discourses often marginalize or essentialize people of so-called “host” countries, while silencing subaltern perspectives, resistance struggles, knowledges, and epistemologies. Challenging current ISL practices in medicine requires educators to actively work towards decolonialization, in part by recognizing the ability of discourses to produce meaning and subjects.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".