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Record W4289518029 · doi:10.1371/journal.pone.0269203

An exploration of anti-corruption and health in international organizations

2022· article· en· W4289518029 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversity of Toronto
KeywordsTransparency (behavior)AccountabilityLanguage changeThematic analysisPublic relationsAuditBusinessSustainable developmentPolitical scienceAccountingSociologyQualitative researchLaw

Abstract

fetched live from OpenAlex

Corruption is a global wicked problem that threatens the achievement of health, social and economic development goals, including Sustainable Development Goal # 3: Ensuring healthy lives and promoting well-being for all. The COVID-19 pandemic and its resulting strain on health systems has heightened risks of corruption both generally and specifically within health systems. Over the past years, international organizations, including those instrumental to the global COVID-19 response, have increased efforts to address corruption within their operations and related programs. However, as attention to anti-corruption efforts is relatively recent within international organizations, there is a lack of literature examining how these organizations address corruption and the impact of their anti-corruption efforts. This study addresses this gap by examining how accountability, transparency, and anti-corruption are taken up by international organizations within their own operations and the reported outcomes of such efforts. The following international organizations were selected as the focus of this document analysis: the World Health Organization, the Global Fund, the United Nations Development Programme, and the World Bank Group. Documents were identified through a targeted search of each organization's website. Documents were then analyzed combining elements of content analysis and thematic analysis. The findings demonstrate that accountability and transparency mechanisms have been employed by each of the four international organizations to address corruption. Further, these organizations commonly employed oversight mechanisms, including risk assessments, investigations, and audits to monitor their internal and external operations for fraud and corruption. All organizations used sanction strategies meant to reprimand identified transgressors and deter future corruption. Findings also demonstrate a marked increase in anti-corruption efforts by these international organizations in recent years. Though this is promising, there remains a distinct absence of evidence demonstrating the impact of such efforts on the prevalence and severity of corruption in international organizations.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

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

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 head0.134
GPT teacher head0.328
Teacher spread0.194 · 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