Bibliographic record
Abstract
Torture and acts of cruel, inhuman, and degrading treatment are among the most widely proscribed violations of international and regional human rights law. Although the UN Human Rights Committee has clarified that this prohibition applies beyond abuses that occur in detention settings, it has rarely been recognized in the context of health care settings. However, a shift is taking place. In early 2011, a coalition of organizations launched the Campaign to Stop Torture in Health Care with the aim of increasing accountability for these abuses. Then, in February 2013, the Special Rapporteur on Torture issued a landmark report, which embraces the paradigm applying the prohibition against torture to health care settings and identifies examples of such abuse and the policies that promote it. Currently, severe abuse is rampant in health care settings, particularly against socially marginalized groups—people living with HIV or tuberculosis, people with disabilities, people who use drugs, sex workers, ethnic minorities, gender and sexual minorities, and people in need of palliative care. Many such abuses appear to rise to the level of torture or ill-treatment. In particular, three broad (and sometimes overlapping) categories of abuse can be identified: (1) forced or coerced medical interventions; (2) denial of care or provision of inferior care on a discriminatory basis; and (3) provision of medical treatment in a humiliating manner. In many cases, torture and ill-treatment in health care are also attended by violations of the right to liberty, particularly in hospitals, tuberculosis centers, drug treatment centers, and mental health facilities. A human rights approach to health care would call for community-based treatment in a number of these instances. Viewing violations in health care settings through an anti-torture lens highlights the particular vulnerability of marginalized groups to abuse, the misuse of medical procedures as a form of social control, and the intersection between torture in health care and the deprivation of liberty. Applying an anti-torture framework also lifts the shroud of darkness that has allowed these abuses to continue with impunity under the guise of medical “expertise” or “necessity.” Additionally, the anti-torture lens is a powerful tool that sets up an immediate and non-derogable obligation for states to remedy these abuses. It thereby can help foster health care settings that genuinely serve as places of care for all people.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.037 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 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".