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Record W2806283206 · doi:10.1186/s12910-018-0284-3

Differences and structural weaknesses of institutional mechanisms for health research ethics: Burkina Faso, Palestine, Peru, and Democratic Republic of the Congo

2018· article· en· W2806283206 on OpenAlexfundno aff
N’koué Emmanuel Sambieni

Bibliographic record

VenueBMC Medical Ethics · 2018
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)MandateResearch ethicsDemocracyPolitical sciencePhilosophy of medicineSocial sciencePublic relationsSociologyMedicineLawPoliticsAlternative medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Regardless of national contexts, the institutions responsible for research ethics, founded on international regulations, are all expected to be structured and to operate in a common way. Our experience with several countries on different continents, however, has raised questions in this regard. This article examines the differences and structural weaknesses of ethics committees in four countries (Burkina Faso, Palestine, Peru, and the Democratic Republic of the Congo) where we have conducted the same socio-anthropological study in the field of reproductive health. METHODS: In addition to recording our observations during field surveys for this study, we performed a documentary review and interviewed expert members of ethics committees, research participants, and researchers who had experience with requesting ethics approvals for research protocols in the field of social sciences and health. RESULTS: The results of this study showed that, despite having the same mandate, the committees functioned differently, while they all exhibited the same weaknesses. Thus, the universalization and standardization of institutional conditions for applying ethical standards in research still present problems that are, at the very least, relevant. CONCLUSION: This study on ethics committees in four countries demonstrated the profound influence of context on the ways in which different institutions function and enforce regulations. In effect, in all social fields, every innovation is infused by its context.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.674
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.674
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.013
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0000.000

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 head0.644
GPT teacher head0.608
Teacher spread0.036 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations36
Published2018
Admission routes1
Has abstractyes

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