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Record W6986272812

Peacekeeper-perpetrated sexual exploitation and abuse, and life satisfaction:A cross-sectional study in Haiti

2023· article· en· W6986272812 on OpenAlexaff

Bibliographic record

VenueUniversity of Birmingham Research Portal (University of Birmingham) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicvaccines and immunoinformatics approaches
Canadian institutionsQueen's University
FundersArts and Humanities Research Council
KeywordsContext (archaeology)Sexual abusePeacekeepingTransactional sexSexual lifeHuman factors and ergonomicsScale (ratio)Human sexuality
DOInot available

Abstract

fetched live from OpenAlex

Peacekeepers from the UN Peacekeeping Operation ‘Mission des Nations Unies pour la stabilisation en Haïti’ (MINUSTAH) have been accused of widespread sexual exploitation and abuse throughout their time in Haiti. However, victims have not received adequate reparations, support, or justice. To date, no research has been done to quantifiably examine how peacekeeper-perpetrated sexual exploitation and abuse (PP-SEA) has affected the lives and well-being of those who have experienced it.<br/>Using multivariate linear regression analysis, this research examines the association between PP-SEA and Satisfaction With Life (SWL) among Haitian community members. Among those who shared third-person micronarratives (n=1588), experiencing PP-SEA was associated with higher average SWL scale scores compared with those who did not. There was no association between PP-SEA and SWL among individuals who shared first-person micronarratives (n=887). Potential contextual factors that may have contributed to these findings<br/>were examined, e.g. the occurrence of transactional sex, nuance in peacekeeper-civilian relationships, and other negative experiences with MINUSTAH within the unexposed group. These results highlight the complexity of the relationship between PP-SEA and SWL in the Haitian context and provide direction for future research.

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)0.001
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 score0.139
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.298
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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