Bibliographic record
Abstract
In situations of conflict, environmental disaster or outbreak of infectious disease, local and national agencies may be unable to adequately respond to the needs of affected populations and individuals. In many locales around the world, international non-governmental organizations (NGOs) provide assistance during acute or protracted crises, and participate in post-crisis recovery and rehabilitation. National and expatriate staff of relief organizations work in varying degrees of partnership with local agencies and other humanitarian actors to address a wide range of needs including health care, nutrition, safe water, sanitation and shelter. Health professionals play key roles in these interventions. This field of health care practice presents distinct practical and ethical challenges for clinicians. As in the case ‘Cholera and Nothing More’ presented by Delan Devakumar, health-related humanitarian projects are often characterized by features such as the prominence of public health concerns, narrow organizational remit and program mandates, widespread limitations of material and human resources, obstacles to creating collaborations among disparate actors, and the cross-cultural and trans-national nature of the care context. These characteristics contribute to the challenge of identifying, and acting upon, parameters of ethically sound health care practice. While there has been some recent discussion of this topic from a bioethics perspective (Eckenwiler, 2003; Benatar, 2006; Fuller, 2006; Macklin, 2006; Sommers-Flanagan, 2007) there is need to further expand and develop bioethics analysis of this field of health care practice (Wikler and Brock, 2007). An inherent challenge of analyzing ethical issues associated with humanitarian work is the complexity, and often instability, of the local health, social and political systems in the settings where these activities take place.
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.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.068 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".