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Record W4313466870

Engaging Ethical Issues Associated with Research and Public Health Interventions During Humanitarian Crises: Review of a Dialogic Workshop

2016· review· en· W4313466870 on OpenAlexaffabout
Anushree Davé, Julie Cumin, Ryoa Chung, Matthew Hunt

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typereview
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalMcGill University
Fundersnot available
KeywordsDialogicPsychological interventionPublic health interventionsPublic healthPolitical sciencePublic relationsEngineering ethicsSociologyPsychologyMedicineNursingEngineeringPedagogy
DOInot available

Abstract

fetched live from OpenAlex

On November 7th, 2014 the Humanitarian Health Ethics Workshop was held at McGill University, in Montreal. Co-hosted by the Montreal Health Equity Research Consortium and the Humanitarian Health Ethics Network, the event included six presentations and extensive discussion amongst participants, including researchers from Canada, Haiti, India, Switzerland and the US. Participants had training in disciplines including anthropology, bioethics, medicine, occupational therapy, philosophy, physical therapy, political science, public administration and public health. The objective of the workshop was to create a forum for discussion amongst scholars and practitioners interested in the ethics of healthcare delivery, research and public health interventions during humanitarian crises. This review is a summary of the presentations given, key themes that emerged during the day’s discussions, and avenues for future research that were identified.

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

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.087
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.087
Threshold uncertainty score0.459

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.118
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.007
Science and technology studies0.0030.005
Scholarly communication0.0070.007
Open science0.0030.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.856
GPT teacher head0.753
Teacher spread0.103 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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