Global health ethics: critical reflections on the contours of an emerging field, 1977–2015
Bibliographic record
Abstract
BACKGROUND: The field of bioethics has evolved over the past half-century, incorporating new domains of inquiry that signal developments in health research, clinical practice, public health in its broadest sense and more recently sensitivity to the interdependence of global health and the environment. These extensions of the reach of bioethics are a welcome response to the growth of global health as a field of vital interest and activity. METHODS: This paper provides a critical interpretive review of how the term "global health ethics" has been used and defined in the literature to date to identify ethical issues that arise and need to be addressed when deliberating on and working to improve the discourse on ethical issues in health globally. RESULTS: Selected publications were analyzed by year of publication and geographical distribution, journal and field, level of engagement, and ethical framework. Of the literature selected, 151 articles (88%) were written by authors in high-income countries (HIC), as defined by the World Bank country classifications, 8 articles (5%) were written by authors in low- or middle-income countries (LMIC), and 13 articles (7%) were collaborations between authors in HIC and LMIC. All of the articles selected except one from 1977 were published after 1998. Literature on global health ethics spiked considerably from the early 2000s, with the highest number in 2011. One hundred twenty-seven articles identified were published in academic journals, 1 document was an official training document, and 44 were chapters in published books. The dominant journals were the American Journal of Bioethics (n = 10), Developing World Bioethics (n = 9), and Bioethics (n = 7). We coded the articles by level of engagement within the ethical domain at different levels: (1) interpersonal, (2) institutional, (3) international, and (4) structural. The ethical frameworks at use corresponded to four functional categories: those examining practical or narrowly applied ethical questions; those concerned with normative ethics; those examining an issue through a single philosophical tradition; and those comparing and contrasting insights from multiple ethical frameworks. CONCLUSIONS: This critical interpretive review is intended to delineate the current contours and revitalize the conversation around the future charge of global health ethics scholarship.
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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 | 0.093 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.012 |
| 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 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".