When Doctoring is not about Doctoring: An Ethical Analysis of Practices Associated with Canadian Immigration HIV Testing
Bibliographic record
Abstract
Immigration medicine and the work carried out by Panel Physicians within the Canadian immigration system give rise to ethically troublesome practices and consequences. In this analysis in three parts, we explore the context of the immigration medical examination, characterize the observed and potential burdens and harms for immigrant and refugee applicants with HIV, and critically assess the possibilities for transforming immigration medical practices and policy to reduce inequities. We use the Code of Ethics of the Canadian Medical Association and the Medical Ethics Manual of the World Medical Association to analyse six practices that fail to meet ethical expectations. This analysis opens up new lines of inquiry into the medico-administrative practices regulating immigration to Canada. It also extends knowledge about the functioning of immigration medical policy and the collection and uses of HIV-related health information. We argue that to reduce burdens and harms to prospective immigrants and refugees to Canada with HIV, changes must occur in how Citizenship and Immigration Canada asks Panel Physicians to work with these applicants. This argument is empirically informed by institutional ethnographic research results of the Canadian immigration system and its treatment of applicants with HIV.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.028 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".